Conversation disentanglement, the task to identify separate threads in conversations, is an important pre-processing step in multi-party conversational NLP applications such as conversational question answering and con-versation summarization. Framing it as a utterance-to-utterance classification problem — i.e. given an utterance of interest (UOI), find which past utterance it replies to — we explore a number of transformer-based models and found that BERT in combination with handcrafted features remains a strong baseline. We then build a multi-task learning model that jointly learns utterance-to-utterance and utterance-to-thread classification. Observing that the ground truth label (past utterance) is in the top candidates when our model makes an error, we experiment with using bipartite graphs as a post-processing step to learn how to best match a set of UOIs to past utterances. Experiments on the Ubuntu IRC dataset show that this approach has the potential to out-perform the conventional greedy approach of simply selecting the highest probability candidate for each UOI independently, indicating a promising future research direction.

Human annotation for establishing the training data is often a very costly process in natural language processing (NLP) tasks, which has led to frugal NLP approaches becoming an important research topic. Many research teams struggle to complete projects with limited funding, labor, and computational resources. Driven by the Move-Step analytic framework theorized in the applied linguistics field, our study offers a rigorous approach to the frugal use of two human annotators to scale up auto-coding for text classification tasks. We applied the Linear Support Vector Machine algorithm to text classification of a job ad corpus. Our Cohen’s Kappa for inter-rater agreement and Area Under the Curve (AUC) values reached averages of 0.76 and 0.80, respectively. The calculated time consumption for our human training process was 36 days. The results indicated that even the strategic and frugal use of only two human annotators could enable the efficient training of classifiers with reasonably good performance. This study does not aim to provide generalizability of the results. Rather, we propose that the annotation strategies arising from this study be considered by our readers only if such strategies are fit for one’s specific research purposes.

Visual question answering (VQA) models, in particular modular ones, are commonly trained on large-scale datasets to achieve state-of-the-art performance. However, such datasets are sometimes not available. Further, it has been shown that training these models on small datasets significantly reduces their accuracy. In this paper, we propose curriculum-based learning (CL) regime to increase the accuracy of VQA models trained on small datasets. Specifically, we offer three criteria to rank the samples in these datasets and propose a training strategy for each criterion. Our results show that, for small datasets, our CL approach yields more accurate results than those obtained when training with no curriculum.

The current study provides a diachronic analysis of the stereotypical portrayals concerning seven of the most prominent foreign nationalities living in Spain in a Spanish news outlet. We use 12 years (2007-2018) of news articles to train word embedding models to quantify the association of such outgroups with drug use, prostitution, crimes, and poverty concepts. Then, we investigate the effects of sociopolitical variables on the computed bias series, such as the outgroup size in the host country and the rate of the population receiving unemployment benefits. Our findings indicate that the texts exhibit bias against foreign-born people, especially in the case of outgroups for which the country of origin has a lower Gross Domestic Product per capita (PPP) than Spain.

Recent years have brought a tremendous growth in assistive robots/prosthetics for people with partial or complete loss of upper limb control. These technologies aim to help the users with various reaching and grasping tasks in their daily lives such as picking up an object and transporting it to a desired location; and their utility critically depends on the ease and effectiveness of communication between the user and robot. One of the natural ways of communicating with assistive technologies is through verbal instructions. The meaning of natural language commands depends on the current configuration of the surrounding environment and needs to be interpreted in this multi-modal context, as accurate interpretation of the command is essential for a successful execution of the user’s intent by an assistive device. The research presented in this paper demonstrates how large-scale situated natural language datasets can support the development of robust assistive technologies. We leveraged a navigational dataset comprising >25k human-provided natural language commands covering diverse situations. We demonstrated a way to extend the dataset in a task-informed way and use it to develop multi-modal intent classifiers for pick and place tasks. Our best classifier reached >98% accuracy in a 16-way multi-modal intent classification task, suggesting high robustness and flexibility.

The detection of hyperbole is an important stepping stone to understanding the intentions of a hyperbolic utterance. We propose a model that combines pre-trained language models with privileged information for the task of hyperbole detection. We also introduce a suite of behavioural tests to probe the capabilities of hyperbole detection models across a range of hyperbole types. Our experiments show that our model improves upon baseline models on an existing hyperbole detection dataset. Probing experiments combined with analysis using local linear approximations (LIME) show that our model excels at detecting one particular type of hyperbole. Further, we discover that our experiments highlight annotation artifacts introduced through the process of literal paraphrasing of hyperbole. These annotation artifacts are likely to be a roadblock to further improvements in hyperbole detection.

Text-pair classification is the task of determining the class relationship between two sentences. It is embedded in several tasks such as paraphrase identification and duplicate question detection. Contemporary methods use fine-tuned transformer encoder semantic representations of the classification token in the text-pair sequence from the transformer’s final layer for class prediction. However, research has shown that earlier parts of the network learn shallow features, such as syntax and structure, which existing methods do not directly exploit. We propose a novel convolution-based decoder for transformer-based architecture that maximizes the use of encoder hidden features for text-pair classification. Our model exploits hidden representations within transformer-based architecture. It outperforms a transformer encoder baseline on average by 50% (relative F1-score) on six datasets from the medical, software engineering, and open-domains. Our work shows that transformer-based models can improve text-pair classification by modifying the fine-tuning step to exploit shallow features while improving model generalization, with only a slight reduction in efficiency.

We investigate the efficiency of two very different spoken term detection approaches for transcription when the available data is insufficient to train a robust speech recognition system. This work is grounded in a very low-resource language documentation scenario where only a few minutes of recording have been transcribed for a given language so far. Experiments on two oral languages show that a pretrained universal phone recognizer, fine-tuned with only a few minutes of target language speech, can be used for spoken term detection through searches in phone confusion networks with a lexicon expressed as a finite state automaton. Experimental results show that a phone recognition based approach provides better overall performances than Dynamic Time Warping when working with clean data, and highlight the benefits of each methods for two types of speech corpus.

Summarisation of reviews aims at compressing opinions expressed in multiple review documents into a concise form while still covering the key opinions. Despite the advancement in summarisation models, evaluation metrics for opinionated text summaries lag behind and still rely on lexical-matching metrics such as ROUGE. In this paper, we propose to use the question-answering(QA) approach to evaluate summaries of opinions in reviews. We propose to identify opinion-bearing text spans in the reference summary to generate QA pairs so as to capture salient opinions. A QA model is then employed to probe the candidate summary to evaluate information overlap between candidate and reference summaries. We show that our metric RunQA, Review Summary Evaluation via Question Answering, correlates well with human judgments in terms of coverage and focus of information. Finally, we design an adversarial task and demonstrate that the proposed approach is more robust than metrics in the literature for ranking summaries.

GPT-2 has been frequently adapted in story generation models as it provides powerful generative capability. However, it still fails to generate consistent stories and lacks diversity. Current story generation models leverage additional information such as plots or commonsense into GPT-2 to guide the generation process. These approaches focus on improving generation quality of stories while our work look at both quality and diversity. We explore combining BERT and GPT-2 to build a variational autoencoder (VAE), and extend it by adding additional objectives to learn global features such as story topic and discourse relations. Our evaluations show our enhanced VAE can provide better quality and diversity trade off, generate less repetitive story content and learn a more informative latent variable.

With the increasing impact of Natural Language Processing tools like topic models in social science research, the experimental rigor and comparability of models and datasets has come under scrutiny. Especially when contributing to research on topics with worldwide impacts like energy policy, objective analyses and reliable datasets are necessary. We contribute toward this goal in two ways: first, we release two diachronic corpora covering 23 years of energy discussions in the U.S. Energy Information Administration. Secondly, we propose a simple and theoretically sound method for automatic topic labelling drawing on political thesauri. We empirically evaluate the quality of our labels, and apply our labelling to topics induced by diachronic topic models on our energy corpora, and present a detailed analysis.

Advancements in Natural Language Generation have raised concerns on its potential misuse for deep fake news. Grover is a model for both generation and detection of neural fake news. While its performance on automatically discriminating neural fake news surpassed GPT-2 and BERT, Grover could face a variety of adversarial attacks to deceive detection. In this work, we present an investigation of Grover’s susceptibility to adversarial attacks such as character-level and word-level perturbations. The experiment results show that even a singular character alteration can cause Grover to fail, affecting up to 97% of target articles with unlimited attack attempts, exposing a lack of robustness. We further analyse these misclassified cases to highlight affected words, identify vulnerability within Grover’s encoder, and perform a novel visualisation of cumulative classification scores to assist in interpreting model behaviour.

Generating long and coherent text is an important and challenging task encompassing many application areas such as summarization, document level machine translation and story generation. Despite the success in modeling intra-sentence coherence, existing long text generation models (e.g., BART and GPT-3) still struggle to maintain a coherent event sequence throughout the generated text. We conjecture that this is because of the difficulty for the model to revise, replace, revoke or delete any part that has been generated by the model. In this paper, we present a novel semi-autoregressive document generation model capable of revising and editing the generated text. Building on recent models by (Gu et al., 2019; Xu and Carpuat, 2020) we propose document generation as a hierarchical Markov decision process with a two level hierarchy, where the high and low level editing programs. We train our model using imitation learning (Hussein et al., 2017) and introduce roll-in policy such that each policy learns on the output of applying the previous action. Experiments applying the proposed approach sheds various insights on the problems of long text generation using our model. We suggest various remedies such as using distilled dataset, designing better attention mechanisms and using autoregressive models as a low level program.

Universal adversarial texts (UATs) refer to short pieces of text units that can largely affect the predictions of NLP models. Recent studies on universal adversarial attacks assume the accessibility of datasets for the task, which is not realistic. We propose two types of Data-Free Adjusted Gradient (DFAG) attacks to show that it is possible to generate effective UATs with only one arbitrary example which could be manually crafted. Based on the proposed DFAG attacks, this paper explores the vulnerability of commonly used NLP models in terms of two factors: network architectures and pre-trained embeddings. Our empirical studies on three text classification datasets reveal that: 1) CNN based models are more extremely vulnerable to UATs while self-attention models show the most robustness, 2) the vulnerability of CNN and LSTM models and robustness of self-attention models could be attributed to whether they rely on training data artifacts for their predictions, and 3) the pre-trained embeddings could expose vulnerability to both universal adversarial attack and the UAT transfer attack.

HESIP is a hybrid explanation system for image predictions that combines sub-symbolic and symbolic machine learning techniques to explain the predictions of image classification tasks. The sub-symbolic component makes a prediction for an image and the symbolic component learns probabilistic symbolic rules in order to explain that prediction. In HESIP, the explanations are generated in controlled natural language from the learned probabilistic rules using a bi-directional logic grammar. In this paper, we present an explanation modification method where a human-in-the-loop can modify an incorrect explanation generated by the HESIP system and afterwards, the modified explanation is used by HESIP to learn a better explanation.

Fine-tuning pre-trained language models for downstream tasks has become a norm for NLP. Recently it is found that intermediate training can improve performance for fine-tuning language models for target tasks, high-level inference tasks such as Question Answering (QA) tend to work best as intermediate tasks. However it is not clear if intermediate training generally benefits various language models. In this paper, using the SQuAD-2.0 QA task for intermediate training for target text classification tasks, we experimented on eight tasks for single-sequence classification and eight tasks for sequence-pair classification using two base and two compact language models. Our experiments show that QA-based intermediate training generates varying transfer performance across different language models, except for similar QA tasks.

We address the question of how to account for both forward and backward dependencies in an online processing account of human language acquisition. We focus on descriptive adjectives in English and Italian, and show that the acquisition of adjectives in these languages likely relies on tracking both forward and backward regularities. Our simulations confirm that forward-predicting models like standard Recurrent Neural Networks (RNN) cannot account for this phenomenon due to the lack of backward prediction, but the addition of a small delay (as proposed in Turek et al., 2019) endows the RNN with the ability to not only predict but also retrodict.

Automatic post-editing (APE) is an important remedy for reducing errors of raw translated texts that are produced by machine translation (MT) systems or software-aided translation. In this paper, we present a systematic approach to tackle the APE task for Vietnamese. Specifically, we construct the first large-scale dataset of 5M Vietnamese translated and corrected sentence pairs. We then apply strong neural MT models to handle the APE task, using our constructed dataset. Experimental results from both automatic and human evaluations show the effectiveness of the neural MT models in handling the Vietnamese APE task.

In developing systems to identify focus entities in scientific literature, we face the problem of discriminating key entities of interest from other potentially relevant entities of the same type mentioned in the articles. We introduce the task of pathogen characterisation. We aim to discriminate mentions of biological pathogens, that are actively studied in the research presented in scientific publications. These are the pathogens that are the focus of direct experimentation in the research, rather than those that are referred to for context or as playing secondary roles. In this paper, we explore the hypothesis that these focus entities can be differentiated from other, non-actively studied, pathogens mentioned in articles through analysis of the patterns of mentions across different sections of a scientific paper, that is, using the discourse structure of the paper. We provide an indicative case study with the help of a small data set of PubMed abstracts that have been annotated with actively mentioned pathogens.

Hierarchical document categorisation is a special case of multi-label document categorisation, where there is a taxonomic hierarchy among the labels. While various approaches have been proposed for hierarchical document categorisation, there is no standard benchmark dataset, resulting in different methods being evaluated independently and there being no empirical consensus on what methods perform best. In this work, we examine different combinations of neural text encoders and hierarchical methods in an end-to-end framework, and evaluate over three datasets. We find that the performance of hierarchical document categorisation is determined not only by how the hierarchical information is modelled, but also the structure of the label hierarchy and class distribution.

In 2019, the Australasian Language Technology Association (ALTA) organised a shared task to detect the target of sarcastic comments posted on social media. However, there were no winners as it proved to be a difficult task. In this work, we revisit the task posted by ALTA by using transformers, specifically BERT, given the current success of the transformer-based model in various NLP tasks. We conducted our experiments on two BERT models (TD-BERT and BERT-AEN). We evaluated our model on the data set provided by ALTA (Reddit) and two additional data sets: ‘book snippets’ and ‘Tweets’. Our results show that our proposed method achieves a 15.2% improvement from the current state-of-the-art system on the Reddit data set and 4% improvement on Tweets.

Transformer encoder models exhibit strong performance in single-domain applications. However, in a cross-domain situation, using a sub-word vocabulary model results in sub-word overlap. This is an issue when there is an overlap between sub-words that share no semantic similarity between domains. We hypothesize that alleviating this overlap allows for a more effective modeling of multi-domain tasks; we consider the biomedical and general domains in this paper. We present a study on reducing sub-word overlap by scaling the vocabulary size in a Transformer encoder model while pretraining with multiple domains. We observe a significant increase in downstream performance in the general-biomedical cross-domain from a reduction in sub-word overlap.

The 2021 ALTA shared task is the 12th instance of a series of shared tasks organised by ALTA since 2010. Motivated by the advances in machine learning in the last 10 years, this year�s task is a re-visit of the 2011 ALTA shared task. Set within the framework of Evidence Based Medicine (EBM), the goal is to predict the qual-ity of the clinical evidence present in a set of documents. This year�s participant results didnot improve over those of participants from 2011.

We describe our methods for automatically grading the level of clinical evidence in medical papers, as part of the ALTA 2021 shared task. We use a combination of transfer learning and a hand-crafted, feature-based classifier. Our system (�orangutanV3�) obtained an accuracy score of 0.4918, which placed third in the leaderboard. From our failure analysis, we find that our classification techniques do not appropriately handle cases when the conclusions of across the medical papers are themselves inconclusive. We believe that this shortcoming can be overcome�thus improving the classification accuracy�by incorporating document similarity techniques.

This paper describes our approach for the automatic grading of evidence task from the Australasian Language Technology Association (ALTA) Shared Task 2021. We developed two classification models with SVM and RoBERTa and applied an ensemble technique to combine the grades from different classifiers. Our results showed that the SVM model achieved comparable results to the RoBERTa model, and the ensemble system outperformed the individual models on this task. Our system achieved the first place among five teams and obtained 3.3% higher accuracy than the second place.

In this paper, we investigate the utility of modern pretrained language models for the evidence grading system in the medical literature based on the ALTA 2021 shared task. We benchmark 1) domain-specific models that are optimized for medical literature and 2) domain-generic models with rich latent discourse representation (i.e. ELECTRA, RoBERTa). Our empirical experiments reveal that these modern pretrained language models suffer from high variance, and the ensemble method can improve the model performance. We found that ELECTRA performs best with an accuracy of 53.6% on the test set, outperforming domain-specific models.1