Byron C. Wallace

Also published as: Byron Wallace


2023

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Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations
Lucy Lu Wang | Yulia Otmakhova | Jay DeYoung | Thinh Hung Truong | Bailey Kuehl | Erin Bransom | Byron Wallace
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Evaluating multi-document summarization (MDS) quality is difficult. This is especially true in the case of MDS for biomedical literature reviews, where models must synthesize contradicting evidence reported across different documents. Prior work has shown that rather than performing the task, models may exploit shortcuts that are difficult to detect using standard n-gram similarity metrics such as ROUGE. Better automated evaluation metrics are needed, but few resources exist to assess metrics when they are proposed. Therefore, we introduce a dataset of human-assessed summary quality facets and pairwise preferences to encourage and support the development of better automated evaluation methods for literature review MDS. We take advantage of community submissions to the Multi-document Summarization for Literature Review (MSLR) shared task to compile a diverse and representative sample of generated summaries. We analyze how automated summarization evaluation metrics correlate with lexical features of generated summaries, to other automated metrics including several we propose in this work, and to aspects of human-assessed summary quality. We find that not only do automated metrics fail to capture aspects of quality as assessed by humans, in many cases the system rankings produced by these metrics are anti-correlated with rankings according to human annotators.

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Revisiting Relation Extraction in the era of Large Language Models
Somin Wadhwa | Silvio Amir | Byron Wallace
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text. Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and then predict the relationship between them. Recent work has instead treated the problem as a sequence-to-sequence task, linearizing relations between entities as target strings to be generated conditioned on the input. Here we push the limits of this approach, using larger language models (GPT-3 and Flan-T5 large) than considered in prior work and evaluating their performance on standard RE tasks under varying levels of supervision. We address issues inherent to evaluating generative approaches to RE by doing human evaluations, in lieu of relying on exact matching. Under this refined evaluation, we find that: (1) Few-shot prompting with GPT-3 achieves near SOTA performance, i.e., roughly equivalent to existing fully supervised models; (2) Flan-T5 is not as capable in the few-shot setting, but supervising and fine-tuning it with Chain-of-Thought (CoT) style explanations (generated via GPT-3) yields SOTA results. We release this model as a new baseline for RE tasks.

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Summarizing, Simplifying, and Synthesizing Medical Evidence using GPT-3 (with Varying Success)
Chantal Shaib | Millicent Li | Sebastian Joseph | Iain Marshall | Junyi Jessy Li | Byron Wallace
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Large language models, particularly GPT-3, are able to produce high quality summaries ofgeneral domain news articles in few- and zero-shot settings. However, it is unclear if such models are similarly capable in more specialized domains such as biomedicine. In this paper we enlist domain experts (individuals with medical training) to evaluate summaries of biomedical articles generated by GPT-3, given no supervision. We consider bothsingle- and multi-document settings. In the former, GPT-3 is tasked with generating regular and plain-language summaries of articles describing randomized controlled trials; in thelatter, we assess the degree to which GPT-3 is able to synthesize evidence reported acrossa collection of articles. We design an annotation scheme for evaluating model outputs, withan emphasis on assessing the factual accuracy of generated summaries. We find that whileGPT-3 is able to summarize and simplify single biomedical articles faithfully, it strugglesto provide accurate aggregations of findings over multiple documents. We release all data,code, and annotations used in this work.

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How Many and Which Training Points Would Need to be Removed to Flip this Prediction?
Jinghan Yang | Sarthak Jain | Byron C. Wallace
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

We consider the problem of identifying a minimal subset of training data 𝒼t such that if the instances comprising 𝒼t had been removed prior to training, the categorization of a given test point xt would have been different.Identifying such a set may be of interest for a few reasons.First, the cardinality of 𝒼t provides a measure of robustness (if |𝒼t| is small for xt, we might be less confident in the corresponding prediction), which we show is correlated with but complementary to predicted probabilities.Second, interrogation of 𝒼t may provide a novel mechanism for contesting a particular model prediction: If one can make the case that the points in 𝒼t are wrongly labeled or irrelevant, this may argue for overturning the associated prediction. Identifying 𝒼t via brute-force is intractable.We propose comparatively fast approximation methods to find 𝒼t based on influence functions, and find that—for simple convex text classification models—these approaches can often successfully identify relatively small sets of training examples which, if removed, would flip the prediction.

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Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges
Sanjana Ramprasad | Jered Mcinerney | Iain Marshall | Byron Wallace
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

In this work we present TrialsSummarizer, a system that aims to automatically summarize evidence presented in the set of randomized controlled trials most relevant to a given query. Building on prior work, the system retrieves trial publications matching a query specifying a combination of condition, intervention(s), and outcome(s), and ranks these according to sample size and estimated study quality.The top-k such studies are passed through a neural multi-document summarization system, yielding a synopsis of these trials. We consider two architectures: A standard sequence-to-sequence model based on BART, and a multi-headed architecture intended to provide greater transparency and controllability to end-users.Both models produce fluent and relevant summaries of evidence retrieved for queries, but their tendency to introduce unsupported statements render them inappropriate for use in this domain at present.The proposed architecture may help users verify outputs allowing users to trace generated tokens back to inputs. The demonstration video can be found at https://vimeo.com/735605060The prototype, source code, and model weights are available at: https://sanjanaramprasad.github.io/trials-summarizer/

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SemEval-2023 Task 8: Causal Medical Claim Identification and Related PIO Frame Extraction from Social Media Posts
Vivek Khetan | Somin Wadhwa | Byron Wallace | Silvio Amir
Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)

Identification of medical claims from user-generated text data is an onerous but essential step for various tasks including content moderation, and hypothesis generation. SemEval-2023 Task 8 is an effort towards building those capabilities and motivating further research in this direction. This paper summarizes the details and results of shared task 8 at SemEval-2023 which involved identifying causal medical claims and extracting related Populations, Interventions, and Outcomes (“PIO”) frames from social media (Reddit) text. This shared task comprised two subtasks: (1) Causal claim identification; and (2) PIO frame extraction. In total, seven teams participated in the task. Of the seven, six provided system descriptions which we summarize here. For the first subtask, the best approach yielded a macro-averaged F-1 score of 78.40, and for the second subtask, the best approach achieved token-level F-1 scores of 40.55 for Populations, 49.71 for Interventions, and 30.08 for Outcome frames.

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RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Media
Somin Wadhwa | Vivek Khetan | Silvio Amir | Byron Wallace
Findings of the Association for Computational Linguistics: EACL 2023

We present Reddit Health Online Talk (RedHOT), a corpus of 22,000 richly annotated social media posts from Reddit spanning 24 health conditions. Annotations include demarcations of spans corresponding to medical claims, personal experiences, and questions.We collect additional granular annotations on identified claims.Specifically, we mark snippets that describe patient Populations, Interventions, and Outcomes (PIO elements) within these. Using this corpus, we introduce the task of retrieving trustworthy evidence relevant to a given claim made on social media. We propose a new method to automatically derive (noisy) supervision for this task which we use to train a dense retrieval model; this outperforms baseline models. Manual evaluation of retrieval results performed by medical doctors indicate that while our system performance is promising, there is considerable room for improvement.We release all annotations collected (and scripts to assemble the dataset), and all code necessary to reproduce the results in this paper at: https://sominw.com/redhot.

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NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization
Junru Lu | Jiazheng Li | Byron Wallace | Yulan He | Gabriele Pergola
Findings of the Association for Computational Linguistics: EACL 2023

Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potential means to address this issue. In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved. In this approach, we first generate reference summaries via sentence matching between the original and the simplified abstracts. These summaries are then used to train an extractive summarizer, learning the most relevant content to be simplified. Then, to ensure the narrative consistency of the simplified text, we synthesize auxiliary narrative prompts combining key phrases derived from the syntactical analyses of the original text. Our model achieves results significantly better than the seq2seq baseline on an English medical corpus, yielding 3% 4% absolute improvements in terms of lexical similarity, and providing a further 1.1% improvement of SARI score when combined with the baseline. We also highlight shortcomings of existing evaluation methods, and introduce new metrics that take into account both lexical and high-level semantic similarity. A human evaluation conducted on a random sample of the test set further establishes the effectiveness of the proposed approach. Codes and models are released here: https://github.com/LuJunru/NapSS.

2022

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Learning to Ask Like a Physician
Eric Lehman | Vladislav Lialin | Katelyn Edelwina Legaspi | Anne Janelle Sy | Patricia Therese Pile | Nicole Rose Alberto | Richard Raymund Ragasa | Corinna Victoria Puyat | Marianne Katharina Taliño | Isabelle Rose Alberto | Pia Gabrielle Alfonso | Dana Moukheiber | Byron Wallace | Anna Rumshisky | Jennifer Liang | Preethi Raghavan | Leo Anthony Celi | Peter Szolovits
Proceedings of the 4th Clinical Natural Language Processing Workshop

Existing question answering (QA) datasets derived from electronic health records (EHR) are artificially generated and consequently fail to capture realistic physician information needs. We present Discharge Summary Clinical Questions (DiSCQ), a newly curated question dataset composed of 2,000+ questions paired with the snippets of text (triggers) that prompted each question. The questions are generated by medical experts from 100+ MIMIC-III discharge summaries. We analyze this dataset to characterize the types of information sought by medical experts. We also train baseline models for trigger detection and question generation (QG), paired with unsupervised answer retrieval over EHRs. Our baseline model is able to generate high quality questions in over 62% of cases when prompted with human selected triggers. We release this dataset (and all code to reproduce baseline model results) to facilitate further research into realistic clinical QA and QG: https://github.com/elehman16/discq.

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Overview of MSLR2022: A Shared Task on Multi-document Summarization for Literature Reviews
Lucy Lu Wang | Jay DeYoung | Byron Wallace
Proceedings of the Third Workshop on Scholarly Document Processing

We provide an overview of the MSLR2022 shared task on multi-document summarization for literature reviews. The shared task was hosted at the Third Scholarly Document Processing (SDP) Workshop at COLING 2022. For this task, we provided data consisting of gold summaries extracted from review papers along with the groups of input abstracts that were synthesized into these summaries, split into two subtasks. In total, six teams participated, making 10 public submissions, 6 to the Cochrane subtask and 4 to the MSˆ2 subtask. The top scoring systems reported over 2 points ROUGE-L improvement on the Cochrane subtask, though performance improvements are not consistently reported across all automated evaluation metrics; qualitative examination of the results also suggests the inadequacy of current evaluation metrics for capturing factuality and consistency on this task. Significant work is needed to improve system performance, and more importantly, to develop better methods for automatically evaluating performance on this task.

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Intermediate Entity-based Sparse Interpretable Representation Learning
Diego Garcia-Olano | Yasumasa Onoe | Joydeep Ghosh | Byron Wallace
Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

Interpretable entity representations (IERs) are sparse embeddings that are “human-readable” in that dimensions correspond to fine-grained entity types and values are predicted probabilities that a given entity is of the corresponding type. These methods perform well in zero-shot and low supervision settings. Compared to standard dense neural embeddings, such interpretable representations may permit analysis and debugging. However, while fine-tuning sparse, interpretable representations improves accuracy on downstream tasks, it destroys the semantics of the dimensions which were enforced in pre-training. Can we maintain the interpretable semantics afforded by IERs while improving predictive performance on downstream tasks? Toward this end, we propose Intermediate enTity-based Sparse Interpretable Representation Learning (ItsIRL). ItsIRL realizes improved performance over prior IERs on biomedical tasks, while maintaining “interpretability” generally and their ability to support model debugging specifically. The latter is enabled in part by the ability to perform “counterfactual” fine-grained entity type manipulation, which we explore in this work. Finally, we propose a method to construct entity type based class prototypes for revealing global semantic properties of classes learned by our model. Code for pre-training and experiments will be made publicly available.

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Combining Feature and Instance Attribution to Detect Artifacts
Pouya Pezeshkpour | Sarthak Jain | Sameer Singh | Byron Wallace
Findings of the Association for Computational Linguistics: ACL 2022

Training the deep neural networks that dominate NLP requires large datasets. These are often collected automatically or via crowdsourcing, and may exhibit systematic biases or annotation artifacts. By the latter we mean spurious correlations between inputs and outputs that do not represent a generally held causal relationship between features and classes; models that exploit such correlations may appear to perform a given task well, but fail on out of sample data. In this paper, we evaluate use of different attribution methods for aiding identification of training data artifacts. We propose new hybrid approaches that combine saliency maps (which highlight important input features) with instance attribution methods (which retrieve training samples influential to a given prediction). We show that this proposed training-feature attribution can be used to efficiently uncover artifacts in training data when a challenging validation set is available. We also carry out a small user study to evaluate whether these methods are useful to NLP researchers in practice, with promising results. We make code for all methods and experiments in this paper available.

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Influence Functions for Sequence Tagging Models
Sarthak Jain | Varun Manjunatha | Byron Wallace | Ani Nenkova
Findings of the Association for Computational Linguistics: EMNLP 2022

Many standard tasks in NLP (e.g., Named Entity Recognition, Part-of-Speech tagging, and Semantic Role Labeling) are naturally framed as sequence tagging problems. However, there has been comparatively little work on interpretability methods for sequence tagging models. In this paper, we extend influence functions — which aim to trace predictions back to the training points that informed them — to sequence tagging tasks. We define the influence of a training instance segment as the effect that perturbing the labels within this segment has on a test segment level prediction. We provide an efficient approximation to compute this, and show that it tracks with the “true” segment influence (measured empirically). We show the practical utility of segment influence by using the method to identify noisy annotations in NER corpora.

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Evaluating Factuality in Text Simplification
Ashwin Devaraj | William Sheffield | Byron Wallace | Junyi Jessy Li
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Automated simplification models aim to make input texts more readable. Such methods have the potential to make complex information accessible to a wider audience, e.g., providing access to recent medical literature which might otherwise be impenetrable for a lay reader. However, such models risk introducing errors into automatically simplified texts, for instance by inserting statements unsupported by the corresponding original text, or by omitting key information. Providing more readable but inaccurate versions of texts may in many cases be worse than providing no such access at all. The problem of factual accuracy (and the lack thereof) has received heightened attention in the context of summarization models, but the factuality of automatically simplified texts has not been investigated. We introduce a taxonomy of errors that we use to analyze both references drawn from standard simplification datasets and state-of-the-art model outputs. We find that errors often appear in both that are not captured by existing evaluation metrics, motivating a need for research into ensuring the factual accuracy of automated simplification models.

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That’s the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data
Jered McInerney | Geoffrey Young | Jan-Willem van de Meent | Byron Wallace
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Pretraining multimodal models on Electronic Health Records (EHRs) provides a means of learning representations that can transfer to downstream tasks with minimal supervision. Recent multimodal models induce soft local alignments between image regions and sentences. This is of particular interest in the medical domain, where alignments might highlight regions in an image relevant to specific phenomena described in free-text. While past work has suggested that attention “heatmaps” can be interpreted in this manner, there has been little evaluation of such alignments. We compare alignments from a state-of-the-art multimodal (image and text) model for EHR with human annotations that link image regions to sentences. Our main finding is that the text has an often weak or unintuitive influence on attention; alignments do not consistently reflect basic anatomical information. Moreover, synthetic modifications — such as substituting “left” for “right” — do not substantially influence highlights. Simple techniques such as allowing the model to opt out of attending to the image and few-shot finetuning show promise in terms of their ability to improve alignments with very little or no supervision. We make our code and checkpoints open-source.

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PHEE: A Dataset for Pharmacovigilance Event Extraction from Text
Zhaoyue Sun | Jiazheng Li | Gabriele Pergola | Byron Wallace | Bino John | Nigel Greene | Joseph Kim | Yulan He
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions. Doing so may in turn prevent or reduce the harm to patients and ultimately improve public health. Evaluating and monitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever growing collection of spontaneous reports from health professionals, physicians, and pharmacists, and information voluntarily submitted by patients. In this scenario, facilitating analysis of such reports via automation has the potential to rapidly identify safety signals. Unfortunately, public resources for developing natural language models for this task are scant. We present PHEE, a novel dataset for pharmacovigilance comprising over 5000 annotated events from medical case reports and biomedical literature, making it the largest such public dataset to date. We describe the hierarchical event schema designed to provide coarse and fine-grained information about patients’ demographics, treatments and (side) effects. Along with the discussion of the dataset, we present a thorough experimental evaluation of current state-of-the-art approaches for biomedical event extraction, point out their limitations, and highlight open challenges to foster future research in this area.

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Self-Repetition in Abstractive Neural Summarizers
Nikita Salkar | Thomas Trikalinos | Byron Wallace | Ani Nenkova
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We provide a quantitative and qualitative analysis of self-repetition in the output of neural summarizers. We measure self-repetition as the number of n-grams of length four or longer that appear in multiple outputs of the same system. We analyze the behavior of three popular architectures (BART, T5, and Pegasus), fine-tuned on five datasets. In a regression analysis, we find that the three architectures have different propensities for repeating content across output summaries for inputs, with BART being particularly prone to self-repetition. Fine-tuning on more abstractive data, and on data featuring formulaic language is associated with a higher rate of self-repetition. In qualitative analysis, we find systems produce artefacts such as ads and disclaimers unrelated to the content being summarized, as well as formulaic phrases common in the fine-tuning domain. Our approach to corpus-level analysis of self-repetition may help practitioners clean up training data for summarizers and ultimately support methods for minimizing the amount of self-repetition.

2021

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What Would it Take to get Biomedical QA Systems into Practice?
Gregory Kell | Iain Marshall | Byron Wallace | Andre Jaun
Proceedings of the 3rd Workshop on Machine Reading for Question Answering

Medical question answering (QA) systems have the potential to answer clinicians’ uncertainties about treatment and diagnosis on-demand, informed by the latest evidence. However, despite the significant progress in general QA made by the NLP community, medical QA systems are still not widely used in clinical environments. One likely reason for this is that clinicians may not readily trust QA system outputs, in part because transparency, trustworthiness, and provenance have not been key considerations in the design of such models. In this paper we discuss a set of criteria that, if met, we argue would likely increase the utility of biomedical QA systems, which may in turn lead to adoption of such systems in practice. We assess existing models, tasks, and datasets with respect to these criteria, highlighting shortcomings of previously proposed approaches and pointing toward what might be more usable QA systems.

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Biomedical Interpretable Entity Representations
Diego Garcia-Olano | Yasumasa Onoe | Ioana Baldini | Joydeep Ghosh | Byron Wallace | Kush Varshney
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Disentangling Representations of Text by Masking Transformers
Xiongyi Zhang | Jan-Willem van de Meent | Byron Wallace
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Representations from large pretrained models such as BERT encode a range of features into monolithic vectors, affording strong predictive accuracy across a range of downstream tasks. In this paper we explore whether it is possible to learn disentangled representations by identifying existing subnetworks within pretrained models that encode distinct, complementary aspects. Concretely, we learn binary masks over transformer weights or hidden units to uncover subsets of features that correlate with a specific factor of variation; this eliminates the need to train a disentangled model from scratch for a particular task. We evaluate this method with respect to its ability to disentangle representations of sentiment from genre in movie reviews, toxicity from dialect in Tweets, and syntax from semantics. By combining masking with magnitude pruning we find that we can identify sparse subnetworks within BERT that strongly encode particular aspects (e.g., semantics) while only weakly encoding others (e.g., syntax). Moreover, despite only learning masks, disentanglement-via-masking performs as well as — and often better than —previously proposed methods based on variational autoencoders and adversarial training.

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Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data
David Lowell | Brian Howard | Zachary C. Lipton | Byron Wallace
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Unsupervised Data Augmentation (UDA) is a semisupervised technique that applies a consistency loss to penalize differences between a model’s predictions on (a) observed (unlabeled) examples; and (b) corresponding ‘noised’ examples produced via data augmentation. While UDA has gained popularity for text classification, open questions linger over which design decisions are necessary and how to extend the method to sequence labeling tasks. In this paper, we re-examine UDA and demonstrate its efficacy on several sequential tasks. Our main contribution is an empirical study of UDA to establish which components of the algorithm confer benefits in NLP. Notably, although prior work has emphasized the use of clever augmentation techniques including back-translation, we find that enforcing consistency between predictions assigned to observed and randomly substituted words often yields comparable (or greater) benefits compared to these more complex perturbation models. Furthermore, we find that applying UDA’s consistency loss affords meaningful gains without any unlabeled data at all, i.e., in a standard supervised setting. In short, UDA need not be unsupervised to realize much of its noted benefits, and does not require complex data augmentation to be effective.

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Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?
Eric Lehman | Sarthak Jain | Karl Pichotta | Yoav Goldberg | Byron Wallace
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Large Transformers pretrained over clinical notes from Electronic Health Records (EHR) have afforded substantial gains in performance on predictive clinical tasks. The cost of training such models (and the necessity of data access to do so) coupled with their utility motivates parameter sharing, i.e., the release of pretrained models such as ClinicalBERT. While most efforts have used deidentified EHR, many researchers have access to large sets of sensitive, non-deidentified EHR with which they might train a BERT model (or similar). Would it be safe to release the weights of such a model if they did? In this work, we design a battery of approaches intended to recover Personal Health Information (PHI) from a trained BERT. Specifically, we attempt to recover patient names and conditions with which they are associated. We find that simple probing methods are not able to meaningfully extract sensitive information from BERT trained over the MIMIC-III corpus of EHR. However, more sophisticated “attacks” may succeed in doing so: To facilitate such research, we make our experimental setup and baseline probing models available at https://github.com/elehman16/exposing_patient_data_release.

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An Empirical Comparison of Instance Attribution Methods for NLP
Pouya Pezeshkpour | Sarthak Jain | Byron Wallace | Sameer Singh
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Widespread adoption of deep models has motivated a pressing need for approaches to interpret network outputs and to facilitate model debugging. Instance attribution methods constitute one means of accomplishing these goals by retrieving training instances that (may have) led to a particular prediction. Influence functions (IF; Koh and Liang 2017) provide machinery for doing this by quantifying the effect that perturbing individual train instances would have on a specific test prediction. However, even approximating the IF is computationally expensive, to the degree that may be prohibitive in many cases. Might simpler approaches (e.g., retrieving train examples most similar to a given test point) perform comparably? In this work, we evaluate the degree to which different potential instance attribution agree with respect to the importance of training samples. We find that simple retrieval methods yield training instances that differ from those identified via gradient-based methods (such as IFs), but that nonetheless exhibit desirable characteristics similar to more complex attribution methods. Code for all methods and experiments in this paper is available at: https://github.com/successar/instance_attributions_NLP.

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On the Impact of Random Seeds on the Fairness of Clinical Classifiers
Silvio Amir | Jan-Willem van de Meent | Byron Wallace
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Recent work has shown that fine-tuning large networks is surprisingly sensitive to changes in random seed(s). We explore the implications of this phenomenon for model fairness across demographic groups in clinical prediction tasks over electronic health records (EHR) in MIMIC-III —— the standard dataset in clinical NLP research. Apparent subgroup performance varies substantially for seeds that yield similar overall performance, although there is no evidence of a trade-off between overall and subgroup performance. However, we also find that the small sample sizes inherent to looking at intersections of minority groups and somewhat rare conditions limit our ability to accurately estimate disparities. Further, we find that jointly optimizing for high overall performance and low disparities does not yield statistically significant improvements. Our results suggest that fairness work using MIMIC-III should carefully account for variations in apparent differences that may arise from stochasticity and small sample sizes.

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Paragraph-level Simplification of Medical Texts
Ashwin Devaraj | Iain Marshall | Byron Wallace | Junyi Jessy Li
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We consider the problem of learning to simplify medical texts. This is important because most reliable, up-to-date information in biomedicine is dense with jargon and thus practically inaccessible to the lay audience. Furthermore, manual simplification does not scale to the rapidly growing body of biomedical literature, motivating the need for automated approaches. Unfortunately, there are no large-scale resources available for this task. In this work we introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinical topics. We then propose a new metric based on likelihood scores from a masked language model pretrained on scientific texts. We show that this automated measure better differentiates between technical and lay summaries than existing heuristics. We introduce and evaluate baseline encoder-decoder Transformer models for simplification and propose a novel augmentation to these in which we explicitly penalize the decoder for producing “jargon” terms; we find that this yields improvements over baselines in terms of readability.

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Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve
Oshin Agarwal | Yinfei Yang | Byron C. Wallace | Ani Nenkova
Computational Linguistics, Volume 47, Issue 1 - March 2021

Named entity recognition systems achieve remarkable performance on domains such as English news. It is natural to ask: What are these models actually learning to achieve this? Are they merely memorizing the names themselves? Or are they capable of interpreting the text and inferring the correct entity type from the linguistic context? We examine these questions by contrasting the performance of several variants of architectures for named entity recognition, with some provided only representations of the context as features. We experiment with GloVe-based BiLSTM-CRF as well as BERT. We find that context does influence predictions, but the main factor driving high performance is learning the named tokens themselves. Furthermore, we find that BERT is not always better at recognizing predictive contexts compared to a BiLSTM-CRF model. We enlist human annotators to evaluate the feasibility of inferring entity types from context alone and find that humans are also mostly unable to infer entity types for the majority of examples on which the context-only system made errors. However, there is room for improvement: A system should be able to recognize any named entity in a predictive context correctly and our experiments indicate that current systems may be improved by such capability. Our human study also revealed that systems and humans do not always learn the same contextual clues, and context-only systems are sometimes correct even when humans fail to recognize the entity type from the context. Finally, we find that one issue contributing to model errors is the use of “entangled” representations that encode both contextual and local token information into a single vector, which can obscure clues. Our results suggest that designing models that explicitly operate over representations of local inputs and context, respectively, may in some cases improve performance. In light of these and related findings, we highlight directions for future work.

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Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations
Chaitanya Shivade | Rashmi Gangadharaiah | Spandana Gella | Sandeep Konam | Shaoqing Yuan | Yi Zhang | Parminder Bhatia | Byron Wallace
Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations

2020

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Evidence Inference 2.0: More Data, Better Models
Jay DeYoung | Eric Lehman | Benjamin Nye | Iain Marshall | Byron C. Wallace
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing

How do we most effectively treat a disease or condition? Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions. Unfortunately, no such database exists; clinical trial results are instead disseminated primarily via lengthy natural language articles. Perusing all such articles would be prohibitively time-consuming for healthcare practitioners; they instead tend to depend on manually compiled systematic reviews of medical literature to inform care. NLP may speed this process up, and eventually facilitate immediate consult of published evidence. The Evidence Inference dataset was recently released to facilitate research toward this end. This task entails inferring the comparative performance of two treatments, with respect to a given outcome, from a particular article (describing a clinical trial) and identifying supporting evidence. For instance: Does this article report that chemotherapy performed better than surgery for five-year survival rates of operable cancers? In this paper, we collect additional annotations to expand the Evidence Inference dataset by 25%, provide stronger baseline models, systematically inspect the errors that these make, and probe dataset quality. We also release an abstract only (as opposed to full-texts) version of the task for rapid model prototyping. The updated corpus, documentation, and code for new baselines and evaluations are available at http://evidence-inference.ebm-nlp.com/.

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ERASER: A Benchmark to Evaluate Rationalized NLP Models
Jay DeYoung | Sarthak Jain | Nazneen Fatema Rajani | Eric Lehman | Caiming Xiong | Richard Socher | Byron C. Wallace
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

State-of-the-art models in NLP are now predominantly based on deep neural networks that are opaque in terms of how they come to make predictions. This limitation has increased interest in designing more interpretable deep models for NLP that reveal the ‘reasoning’ behind model outputs. But work in this direction has been conducted on different datasets and tasks with correspondingly unique aims and metrics; this makes it difficult to track progress. We propose the Evaluating Rationales And Simple English Reasoning (ERASER a benchmark to advance research on interpretable models in NLP. This benchmark comprises multiple datasets and tasks for which human annotations of “rationales” (supporting evidence) have been collected. We propose several metrics that aim to capture how well the rationales provided by models align with human rationales, and also how faithful these rationales are (i.e., the degree to which provided rationales influenced the corresponding predictions). Our hope is that releasing this benchmark facilitates progress on designing more interpretable NLP systems. The benchmark, code, and documentation are available at https://www.eraserbenchmark.com/

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Learning to Faithfully Rationalize by Construction
Sarthak Jain | Sarah Wiegreffe | Yuval Pinter | Byron C. Wallace
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In many settings it is important for one to be able to understand why a model made a particular prediction. In NLP this often entails extracting snippets of an input text ‘responsible for’ corresponding model output; when such a snippet comprises tokens that indeed informed the model’s prediction, it is a faithful explanation. In some settings, faithfulness may be critical to ensure transparency. Lei et al. (2016) proposed a model to produce faithful rationales for neural text classification by defining independent snippet extraction and prediction modules. However, the discrete selection over input tokens performed by this method complicates training, leading to high variance and requiring careful hyperparameter tuning. We propose a simpler variant of this approach that provides faithful explanations by construction. In our scheme, named FRESH, arbitrary feature importance scores (e.g., gradients from a trained model) are used to induce binary labels over token inputs, which an extractor can be trained to predict. An independent classifier module is then trained exclusively on snippets provided by the extractor; these snippets thus constitute faithful explanations, even if the classifier is arbitrarily complex. In both automatic and manual evaluations we find that variants of this simple framework yield predictive performance superior to ‘end-to-end’ approaches, while being more general and easier to train. Code is available at https://github.com/successar/FRESH.

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Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions
Xiaochuang Han | Byron C. Wallace | Yulia Tsvetkov
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Modern deep learning models for NLP are notoriously opaque. This has motivated the development of methods for interpreting such models, e.g., via gradient-based saliency maps or the visualization of attention weights. Such approaches aim to provide explanations for a particular model prediction by highlighting important words in the corresponding input text. While this might be useful for tasks where decisions are explicitly influenced by individual tokens in the input, we suspect that such highlighting is not suitable for tasks where model decisions should be driven by more complex reasoning. In this work, we investigate the use of influence functions for NLP, providing an alternative approach to interpreting neural text classifiers. Influence functions explain the decisions of a model by identifying influential training examples. Despite the promise of this approach, influence functions have not yet been extensively evaluated in the context of NLP, a gap addressed by this work. We conduct a comparison between influence functions and common word-saliency methods on representative tasks. As suspected, we find that influence functions are particularly useful for natural language inference, a task in which ‘saliency maps’ may not have clear interpretation. Furthermore, we develop a new quantitative measure based on influence functions that can reveal artifacts in training data.

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Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time
Benjamin Nye | Ani Nenkova | Iain Marshall | Byron C. Wallace
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We introduce Trialstreamer, a living database of clinical trial reports. Here we mainly describe the evidence extraction component; this extracts from biomedical abstracts key pieces of information that clinicians need when appraising the literature, and also the relations between these. Specifically, the system extracts descriptions of trial participants, the treatments compared in each arm (the interventions), and which outcomes were measured. The system then attempts to infer which interventions were reported to work best by determining their relationship with identified trial outcome measures. In addition to summarizing individual trials, these extracted data elements allow automatic synthesis of results across many trials on the same topic. We apply the system at scale to all reports of randomized controlled trials indexed in MEDLINE, powering the automatic generation of evidence maps, which provide a global view of the efficacy of different interventions combining data from all relevant clinical trials on a topic. We make all code and models freely available alongside a demonstration of the web interface.

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Proceedings of the First Workshop on Natural Language Processing for Medical Conversations
Parminder Bhatia | Steven Lin | Rashmi Gangadharaiah | Byron Wallace | Izhak Shafran | Chaitanya Shivade | Nan Du | Mona Diab
Proceedings of the First Workshop on Natural Language Processing for Medical Conversations

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Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020
Karin Verspoor | Kevin Bretonnel Cohen | Mark Dredze | Emilio Ferrara | Jonathan May | Robert Munro | Cecile Paris | Byron Wallace
Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020

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Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
Karin Verspoor | Kevin Bretonnel Cohen | Michael Conway | Berry de Bruijn | Mark Dredze | Rada Mihalcea | Byron Wallace
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

2019

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Practical Obstacles to Deploying Active Learning
David Lowell | Zachary C. Lipton | Byron C. Wallace
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. In AL, one iteratively selects training examples for annotation, often those for which the current model is most uncertain (by some measure). The hope is that active sampling leads to better performance than would be achieved under independent and identically distributed (i.i.d.) random samples. While AL has shown promise in retrospective evaluations, these studies often ignore practical obstacles to its use. In this paper, we show that while AL may provide benefits when used with specific models and for particular domains, the benefits of current approaches do not generalize reliably across models and tasks. This is problematic because in practice, one does not have the opportunity to explore and compare alternative AL strategies. Moreover, AL couples the training dataset with the model used to guide its acquisition. We find that subsequently training a successor model with an actively-acquired dataset does not consistently outperform training on i.i.d. sampled data. Our findings raise the question of whether the downsides inherent to AL are worth the modest and inconsistent performance gains it tends to afford.

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Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction
Yinfei Yang | Oshin Agarwal | Chris Tar | Byron C. Wallace | Ani Nenkova
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Modern NLP systems require high-quality annotated data. For specialized domains, expert annotations may be prohibitively expensive; the alternative is to rely on crowdsourcing to reduce costs at the risk of introducing noise. In this paper we demonstrate that directly modeling instance difficulty can be used to improve model performance and to route instances to appropriate annotators. Our difficulty prediction model combines two learned representations: a ‘universal’ encoder trained on out of domain data, and a task-specific encoder. Experiments on a complex biomedical information extraction task using expert and lay annotators show that: (i) simply excluding from the training data instances predicted to be difficult yields a small boost in performance; (ii) using difficulty scores to weight instances during training provides further, consistent gains; (iii) assigning instances predicted to be difficult to domain experts is an effective strategy for task routing. Further, our experiments confirm the expectation that for such domain-specific tasks expert annotations are of much higher quality and preferable to obtain if practical and that augmenting small amounts of expert data with a larger set of lay annotations leads to further improvements in model performance.

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Attention is not Explanation
Sarthak Jain | Byron C. Wallace
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Attention mechanisms have seen wide adoption in neural NLP models. In addition to improving predictive performance, these are often touted as affording transparency: models equipped with attention provide a distribution over attended-to input units, and this is often presented (at least implicitly) as communicating the relative importance of inputs. However, it is unclear what relationship exists between attention weights and model outputs. In this work we perform extensive experiments across a variety of NLP tasks that aim to assess the degree to which attention weights provide meaningful “explanations” for predictions. We find that they largely do not. For example, learned attention weights are frequently uncorrelated with gradient-based measures of feature importance, and one can identify very different attention distributions that nonetheless yield equivalent predictions. Our findings show that standard attention modules do not provide meaningful explanations and should not be treated as though they do.

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Inferring Which Medical Treatments Work from Reports of Clinical Trials
Eric Lehman | Jay DeYoung | Regina Barzilay | Byron C. Wallace
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

How do we know if a particular medical treatment actually works? Ideally one would consult all available evidence from relevant clinical trials. Unfortunately, such results are primarily disseminated in natural language scientific articles, imposing substantial burden on those trying to make sense of them. In this paper, we present a new task and corpus for making this unstructured published scientific evidence actionable. The task entails inferring reported findings from a full-text article describing randomized controlled trials (RCT) with respect to a given intervention, comparator, and outcome of interest, e.g., inferring if a given article provides evidence supporting the use of aspirin to reduce risk of stroke, as compared to placebo. We present a new corpus for this task comprising 10,000+ prompts coupled with full-text articles describing RCTs. Results using a suite of baseline models — ranging from heuristic (rule-based) approaches to attentive neural architectures — demonstrate the difficulty of the task, which we believe largely owes to the lengthy, technical input texts. To facilitate further work on this important, challenging problem we make the corpus, documentation, a website and leaderboard, and all source code for baselines and evaluation publicly available.

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An Analysis of Attention over Clinical Notes for Predictive Tasks
Sarthak Jain | Ramin Mohammadi | Byron C. Wallace
Proceedings of the 2nd Clinical Natural Language Processing Workshop

The shift to electronic medical records (EMRs) has engendered research into machine learning and natural language technologies to analyze patient records, and to predict from these clinical outcomes of interest. Two observations motivate our aims here. First, unstructured notes contained within EMR often contain key information, and hence should be exploited by models. Second, while strong predictive performance is important, interpretability of models is perhaps equally so for applications in this domain. Together, these points suggest that neural models for EMR may benefit from incorporation of attention over notes, which one may hope will both yield performance gains and afford transparency in predictions. In this work we perform experiments to explore this question using two EMR corpora and four different predictive tasks, that: (i) inclusion of attention mechanisms is critical for neural encoder modules that operate over notes fields in order to yield competitive performance, but, (ii) unfortunately, while these boost predictive performance, it is decidedly less clear whether they provide meaningful support for predictions.

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Browsing Health: Information Extraction to Support New Interfaces for Accessing Medical Evidence
Soham Parikh | Elizabeth Conrad | Oshin Agarwal | Iain Marshall | Byron Wallace | Ani Nenkova
Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications

Standard paradigms for search do not work well in the medical context. Typical information needs, such as retrieving a full list of medical interventions for a given condition, or finding the reported efficacy of a particular treatment with respect to a specific outcome of interest cannot be straightforwardly posed in typical text-box search. Instead, we propose faceted-search in which a user specifies a condition and then can browse treatments and outcomes that have been evaluated. Choosing from these, they can access randomized control trials (RCTs) describing individual studies. Realizing such a view of the medical evidence requires information extraction techniques to identify the population, interventions, and outcome measures in an RCT. Patients, health practitioners, and biomedical librarians all stand to benefit from such innovation in search of medical evidence. We present an initial prototype of such an interface applied to pre-registered clinical studies. We also discuss pilot studies into the applicability of information extraction methods to allow for similar access to all published trial results.

2018

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A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature
Benjamin Nye | Junyi Jessy Li | Roma Patel | Yinfei Yang | Iain Marshall | Ani Nenkova | Byron Wallace
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the ‘PICO’ elements). These spans are further annotated at a more granular level, e.g., individual interventions within them are marked and mapped onto a structured medical vocabulary. We acquired annotations from a diverse set of workers with varying levels of expertise and cost. We describe our data collection process and the corpus itself in detail. We then outline a set of challenging NLP tasks that would aid searching of the medical literature and the practice of evidence-based medicine.

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Syntactic Patterns Improve Information Extraction for Medical Search
Roma Patel | Yinfei Yang | Iain Marshall | Ani Nenkova | Byron Wallace
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Medical professionals search the published literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s) of interest. In this paper we demonstrate how features encoding syntactic patterns improve the performance of state-of-the-art sequence tagging models (both neural and linear) for information extraction of these medically relevant categories. We present an analysis of the type of patterns exploited and of the semantic space induced for these, i.e., the distributed representations learned for identified multi-token patterns. We show that these learned representations differ substantially from those of the constituent unigrams, suggesting that the patterns capture contextual information that is otherwise lost.

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Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding
Gaurav Singh | James Thomas | Iain Marshall | John Shawe-Taylor | Byron C. Wallace
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i.e., an ontology). We treat this as a special case of sequence-to-sequence learning in which the decoder begins at the root node of an ontological tree and recursively elects to expand child nodes as a function of the input text, the current node, and the latent decoder state. We demonstrate that this method yields state-of-the-art results on the important task of assigning MeSH terms to biomedical abstracts.

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Learning Disentangled Representations of Texts with Application to Biomedical Abstracts
Sarthak Jain | Edward Banner | Jan-Willem van de Meent | Iain J. Marshall | Byron C. Wallace
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We propose a method for learning disentangled representations of texts that code for distinct and complementary aspects, with the aim of affording efficient model transfer and interpretability. To induce disentangled embeddings, we propose an adversarial objective based on the (dis)similarity between triplets of documents with respect to specific aspects. Our motivating application is embedding biomedical abstracts describing clinical trials in a manner that disentangles the populations, interventions, and outcomes in a given trial. We show that our method learns representations that encode these clinically salient aspects, and that these can be effectively used to perform aspect-specific retrieval. We demonstrate that the approach generalizes beyond our motivating application in experiments on two multi-aspect review corpora.

2017

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Aggregating and Predicting Sequence Labels from Crowd Annotations
An Thanh Nguyen | Byron Wallace | Junyi Jessy Li | Ani Nenkova | Matthew Lease
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text. Given such annotations, we consider two complementary tasks: (1) aggregating sequential crowd labels to infer a best single set of consensus annotations; and (2) using crowd annotations as training data for a model that can predict sequences in unannotated text. For aggregation, we propose a novel Hidden Markov Model variant. To predict sequences in unannotated text, we propose a neural approach using Long Short Term Memory. We evaluate a suite of methods across two different applications and text genres: Named-Entity Recognition in news articles and Information Extraction from biomedical abstracts. Results show improvement over strong baselines. Our source code and data are available online.

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Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization
Ye Zhang | Matthew Lease | Byron C. Wallace
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

A fundamental advantage of neural models for NLP is their ability to learn representations from scratch. However, in practice this often means ignoring existing external linguistic resources, e.g., WordNet or domain specific ontologies such as the Unified Medical Language System (UMLS). We propose a general, novel method for exploiting such resources via weight sharing. Prior work on weight sharing in neural networks has considered it largely as a means of model compression. In contrast, we treat weight sharing as a flexible mechanism for incorporating prior knowledge into neural models. We show that this approach consistently yields improved performance on classification tasks compared to baseline strategies that do not exploit weight sharing.

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Automating Biomedical Evidence Synthesis: RobotReviewer
Iain Marshall | Joël Kuiper | Edward Banner | Byron C. Wallace
Proceedings of ACL 2017, System Demonstrations

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A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification
Ye Zhang | Byron Wallace
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (Kim, 2014; Kalchbrenner et al., 2014; Johnson and Zhang, 2014; Zhang et al., 2016). However, these models require practitioners to specify an exact model architecture and set accompanying hyperparameters, including the filter region size, regularization parameters, and so on. It is currently unknown how sensitive model performance is to changes in these configurations for the task of sentence classification. We thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of architecture components on model performance; our aim is to distinguish between important and comparatively inconsequential design decisions for sentence classification. We focus on one-layer CNNs (to the exclusion of more complex models) due to their comparative simplicity and strong empirical performance, which makes it a modern standard baseline method akin to Support Vector Machine (SVMs) and logistic regression. We derive practical advice from our extensive empirical results for those interested in getting the most out of CNNs for sentence classification in real world settings.

2016

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Rationale-Augmented Convolutional Neural Networks for Text Classification
Ye Zhang | Iain Marshall | Byron C. Wallace
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Modelling Context with User Embeddings for Sarcasm Detection in Social Media
Silvio Amir | Byron C. Wallace | Hao Lyu | Paula Carvalho | MĂĄrio J. Silva
Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning

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MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification
Ye Zhang | Stephen Roller | Byron C. Wallace
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Retrofitting Word Vectors of MeSH Terms to Improve Semantic Similarity Measures
Zhiguo Yu | Trevor Cohen | Byron Wallace | Elmer Bernstam | Todd Johnson
Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis

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Leveraging coreference to identify arms in medical abstracts: An experimental study
Elisa Ferracane | Iain Marshall | Byron C. Wallace | Katrin Erk
Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis

2015

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Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment
Byron C. Wallace | Do Kook Choe | Eugene Charniak
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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Humans Require Context to Infer Ironic Intent (so Computers Probably do, too)
Byron C. Wallace | Do Kook Choe | Laura Kertz | Eugene Charniak
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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A Generative Joint, Additive, Sequential Model of Topics and Speech Acts in Patient-Doctor Communication
Byron C. Wallace | Thomas A. Trikalinos | M. Barton Laws | Ira B. Wilson | Eugene Charniak
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2012

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Multiple Narrative Disentanglement: Unraveling Infinite Jest
Byron Wallace
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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