Tyler A. Chang, Kishaloy Halder, Neha Anna John, Yogarshi Vyas, Yassine Benajiba, Miguel Ballesteros, Dan Roth. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Katerina Margatina, Shuai Wang, Yogarshi Vyas, Neha Anna John, Yassine Benajiba, Miguel Ballesteros. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.

Karthikeyan K, Yogarshi Vyas, Jie Ma, Giovanni Paolini, Neha John, Shuai Wang, Yassine Benajiba, Vittorio Castelli, Dan Roth, Miguel Ballesteros. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

Sharon Levy, Neha John, Ling Liu, Yogarshi Vyas, Jie Ma, Yoshinari Fujinuma, Miguel Ballesteros, Vittorio Castelli, Dan Roth. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

In entity linking, mentions of named entities in raw text are disambiguated against a knowledge base (KB).This work focuses on linking to unseen KBs that do not have training data and whose schema is unknown during training.Our approach relies on methods to flexibly convert entities with several attribute-value pairs from arbitrary KBs into flat strings, which we use in conjunction with state-of-the-art models for zero-shot linking.We further improve the generalization of our model using two regularization schemes based on shuffling of entity attributes and handling of unseen attributes.Experiments on English datasets where models are trained on the CoNLL dataset, and tested on the TAC-KBP 2010 dataset show that our models are 12% (absolute) more accurate than baseline models that simply flatten entities from the target KB.Unlike prior work, our approach also allows for seamlessly combining multiple training datasets.We test this ability by adding both a completely different dataset (Wikia), as well as increasing amount of training data from the TAC-KBP 2010 training set.Our models are more accurate across the board compared to baselines.

Abstract Measuring bias is key for better understanding and addressing unfairness in NLP/ML models. This is often done via fairness metrics, which quantify the differences in a model’s behaviour across a range of demographic groups. In this work, we shed more light on the differences and similarities between the fairness metrics used in NLP. First, we unify a broad range of existing metrics under three generalized fairness metrics, revealing the connections between them. Next, we carry out an extensive empirical comparison of existing metrics and demonstrate that the observed differences in bias measurement can be systematically explained via differences in parameter choices for our generalized metrics.

Miguel Ballesteros, Rishita Anubhai, Shuai Wang, Nima Pourdamghani, Yogarshi Vyas, Jie Ma, Parminder Bhatia, Kathleen McKeown, Yaser Al-Onaizan. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.

Yogarshi Vyas, Marine Carpuat. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Shyam Upadhyay, Yogarshi Vyas, Marine Carpuat, Dan Roth. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Yogarshi Vyas, Xing Niu, Marine Carpuat. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Visual narrative is often a combination of explicit information and judicious omissions, relying on the viewer to supply missing details. In comics, most movements in time and space are hidden in the gutters between panels. To follow the story, readers logically connect panels together by inferring unseen actions through a process called closure. While computers can now describe the content of natural images, in this paper we examine whether they can understand the closure-driven narratives conveyed by stylized artwork and dialogue in comic book panels. We collect a dataset, COMICS, that consists of over 1.2 million panels (120 GB) paired with automatic textbox transcriptions. An in-depth analysis of COMICS demonstrates that neither text nor image alone can tell a comic book story, so a computer must understand both modalities to keep up with the plot. We introduce three cloze-style tasks that ask models to predict narrative and character-centric aspects of a panel given n preceding panels as context. Various deep neural architectures underperform human baselines on these tasks, suggesting that COMICS contains fundamental challenges for both vision and language.

We introduce the task of cross-lingual lexical entailment, which aims to detect whether the meaning of a word in one language can be inferred from the meaning of a word in another language.We construct a gold standard for this task, and propose an unsupervised solution based on distributional word representations.As commonly done in the monolingual setting, we assume a word e entails a word f if the prominent context features of e are a subset of those of f .To address the challenge of comparing contexts across languages, we propose a novel method for inducing sparse bilingual word representations from monolingual and parallel texts.Our approach yields an Fscore of 70%, and significantly outperforms strong baselines based on translation and on existing word representations.

Code-mixing is frequently observed in user generated content on social media, especially from multilingual users. The linguistic complexity of such content is compounded by presence of spelling vari-ations, transliteration and non-adherance to formal grammar. We describe our initial efforts to create a multi-level an-notated corpus of Hindi-English code-mixed text collated from Facebook fo-rums, and explore language identifica-tion, back-transliteration, normalization and POS tagging of this data. Our re-sults show that language identification and transliteration for Hindi are two major challenges that impact POS tagging accu-racy. 1

We present a generic method for augmenting unsupervised query segmentation by incorporating Parts-of-Speech (POS) sequence information to detect meaningful but rare n-grams. Our initial experiments with an existing English POS tagger employing two different POS tagsets and an unsupervised POS induction technique specifically adapted for queries show that POS information can significantly improve query segmentation performance in all these cases.