Randy Goebel


Interactive Rationale Extraction for Text Classification
Jiayi Dai | Mi-Young Kim | Randy Goebel
Proceedings of the The 20th Annual Workshop of the Australasian Language Technology Association

Locally Distributed Activation Vectors for Guided Feature Attribution
Housam K. B. Bashier | Mi-Young Kim | Randy Goebel
Proceedings of the 29th International Conference on Computational Linguistics

Explaining the predictions of a deep neural network (DNN) is a challenging problem. Many attempts at interpreting those predictions have focused on attribution-based methods, which assess the contributions of individual features to each model prediction. However, attribution-based explanations do not always provide faithful explanations to the target model, e.g., noisy gradients can result in unfaithful feature attribution for back-propagation methods. We present a method to learn explanations-specific representations while constructing deep network models for text classification. These representations can be used to faithfully interpret black-box predictions, i.e., highlighting the most important input features and their role in any particular prediction. We show that learning specific representations improves model interpretability across various tasks, for both qualitative and quantitative evaluations, while preserving predictive performance.

DeepBlues@LT-EDI-ACL2022: Depression level detection modelling through domain specific BERT and short text Depression classifiers
Nawshad Farruque | Osmar Zaiane | Randy Goebel | Sudhakar Sivapalan
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

We discuss a variety of approaches to build a robust Depression level detection model from longer social media posts (i.e., Reddit Depression forum posts) using a mental health text pre-trained BERT model. Further, we report our experimental results based on a strategy to select excerpts from long text and then fine-tune the BERT model to combat the issue of memory constraints while processing such texts. We show that, with domain specific BERT, we can achieve reasonable accuracy with fixed text size (in this case 200 tokens) for this task. In addition we can use short text classifiers to extract relevant text from the long text and achieve slightly better accuracy, albeit, trading off with the processing time for extracting such excerpts.


DISK-CSV: Distilling Interpretable Semantic Knowledge with a Class Semantic Vector
Housam Khalifa Bashier | Mi-Young Kim | Randy Goebel
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Neural networks (NN) applied to natural language processing (NLP) are becoming deeper and more complex, making them increasingly difficult to understand and interpret. Even in applications of limited scope on fixed data, the creation of these complex “black-boxes” creates substantial challenges for debugging, understanding, and generalization. But rapid development in this field has now lead to building more straightforward and interpretable models. We propose a new technique (DISK-CSV) to distill knowledge concurrently from any neural network architecture for text classification, captured as a lightweight interpretable/explainable classifier. Across multiple datasets, our approach achieves better performance than the target black-box. In addition, our approach provides better explanations than existing techniques.


RANCC: Rationalizing Neural Networks via Concept Clustering
Housam Khalifa Bashier | Mi-Young Kim | Randy Goebel
Proceedings of the 28th International Conference on Computational Linguistics

We propose a new self-explainable model for Natural Language Processing (NLP) text classification tasks. Our approach constructs explanations concurrently with the formulation of classification predictions. To do so, we extract a rationale from the text, then use it to predict a concept of interest as the final prediction. We provide three types of explanations: 1) rationale extraction, 2) a measure of feature importance, and 3) clustering of concepts. In addition, we show how our model can be compressed without applying complicated compression techniques. We experimentally demonstrate our explainability approach on a number of well-known text classification datasets.


Paraphrase for Open Question Answering: New Dataset and Methods
Ying Xu | Pascual Martínez-Gómez | Yusuke Miyao | Randy Goebel
Proceedings of the Workshop on Human-Computer Question Answering


A Lexicalized Tree Kernel for Open Information Extraction
Ying Xu | Christoph Ringlstetter | Mi-Young Kim | Grzegorz Kondrak | Randy Goebel | Yusuke Miyao
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)


Open Information Extraction with Tree Kernels
Ying Xu | Mi-Young Kim | Kevin Quinn | Randy Goebel | Denilson Barbosa
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


Using Visual Information to Predict Lexical Preference
Shane Bergsma | Randy Goebel
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011


Application of the Tightness Continuum Measure to Chinese Information Retrieval
Ying Xu | Randy Goebel | Christoph Ringlstetter | Grzegorz Kondrak
Proceedings of the 2010 Workshop on Multiword Expressions: from Theory to Applications


Glen, Glenda or Glendale: Unsupervised and Semi-supervised Learning of English Noun Gender
Shane Bergsma | Dekang Lin | Randy Goebel
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

A Continuum-Based Approach for Tightness Analysis of Chinese Semantic Units
Ying Xu | Christoph Ringlstetter | Randy Goebel
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2


Targeting Chinese Nominal Compounds in Corpora
Weiruo Qu | Christoph Ringlstetter | Randy Goebel
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

For compounding languages, a great part of the topical semantics is transported via nominal compounds. Various applications of natural language processing can profit from explicit access to these compounds, provided by a lexicon. The best way to acquire such a resource is to harvest corpora that represent the domain in question. For Chinese, a significant difficulty lies in the fact that the text comes as a string of characters, only segmented by sentence boundaries. Extraction algorithms that solely rely on context variety do not perform precisely enough. We propose a pipeline of filters that starts from a candidate set established by accessor variety and then employs several methods to improve precision. For the experiments the Xinhua part of the Chinese Gigaword Corpus was used. We extracted a random sample of 200 story texts with 119,509 Hanzi characters. All compound words of this evaluation corpus were tagged, segmented into their morphemes, and augmented with the POS-information of their segments. A cascade of filters applied to a preliminary set of compound candidates led to a very high precision of over 90%, measured for the types. The result also holds for a small corpus where a solely contextual method introduces too much noise, even for the longer compounds. An introduction of MI into the basic candidacy algorithm led to a much higher recall with still reasonable precision for subsequent manual processing. Especially for the four-character compounds, that in our sample represent over 40% of the target data, the method has sufficient efficacy to support the rapid construction of compound dictionaries from domain corpora.

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Distributional Identification of Non-Referential Pronouns
Shane Bergsma | Dekang Lin | Randy Goebel
Proceedings of ACL-08: HLT

Discriminative Learning of Selectional Preference from Unlabeled Text
Shane Bergsma | Dekang Lin | Randy Goebel
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing