Jackie Cheung

Ines Arous

Collaborating Alumni - McGill University

PhD - McGill University

Collaborating researcher

Nadhem Benhadjali

Collaborating researcher

Meng (Caden) Cao

Collaborating Alumni - McGill University

Aishik Chakraborty

PhD - McGill University

Khaoula Chehbouni

PhD - McGill University

Principal supervisor :

Master's Research - McGill University

Nelson Filipe Costa

Collaborating researcher - Concordia University University

Maxime Darrin

PhD - McGill University

Co-supervisor :

PhD - McGill University

Aylin Erman

PhD - McGill University

Co-supervisor :

Dan Poenaru

Ori Ernst

Collaborating Alumni - McGill University

Master's Research - McGill University

Jie Gao

Collaborating researcher - McGill University University

Co-supervisor :

Nikki Lobczowski

Langlois Henri

Master's Research - Paris-Saclay University

Principal supervisor :

Pablo Piantanida

Fanny JOURDAN

Postdoctorate - École de technologie suprérieure

Principal supervisor :

Pablo Piantanida

Zichao Li

PhD - McGill University

Principal supervisor :

Siva Reddy

Caleb Moses

PhD - McGill University

Ian Porada

PhD - McGill University

Sihan Qin

Undergraduate - McGill University

Michael Runningwolf

PhD - McGill University

Sina Salmannia

Undergraduate - McGill University

Cesare Spinoso-Di Piano

PhD - McGill University

Sihui Wei

Undergraduate - McGill University

Michael Yu

Collaborating researcher - McGill University University

Co-supervisor :

Nikki Lobczowski

Xiyuan Zou

Master's Research - McGill University

Publications

Characterizing Idioms: Conventionality and Contingency

Michaela Socolof

Michael Wagner

Timothy O'Donnell

Idioms are unlike most phrases in two important ways. First, words in an idiom have non-canonical meanings. Second, the non-canonical meanin… (see more)gs of words in an idiom are contingent on the presence of other words in the idiom. Linguistic theories differ on whether these properties depend on one another, as well as whether special theoretical machinery is needed to accommodate idioms. We define two measures that correspond to the properties above, and we show that idioms fall at the expected intersection of the two dimensions, but that the dimensions themselves are not correlated. Our results suggest that introducing special machinery to handle idioms may not be warranted.

2022-05-01

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (published)

Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization

Meng Cao

Yue Dong

2022-05-01

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (published)

Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment

2022-05-01

Findings of the Association for Computational Linguistics: ACL 2022 (published)

Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment

Most research on question answering focuses on the pre-deployment stage; i.e., building an accurate model for deployment.In this paper, we a… (see more)sk the question: Can we improve QA systems further post-deployment based on user interactions? We focus on two kinds of improvements: 1) improving the QA system’s performance itself, and 2) providing the model with the ability to explain the correctness or incorrectness of an answer.We collect a retrieval-based QA dataset, FeedbackQA, which contains interactive feedback from users. We collect this dataset by deploying a base QA system to crowdworkers who then engage with the system and provide feedback on the quality of its answers.The feedback contains both structured ratings and unstructured natural language explanations.We train a neural model with this feedback data that can generate explanations and re-score answer candidates. We show that feedback data not only improves the accuracy of the deployed QA system but also other stronger non-deployed systems. The generated explanations also help users make informed decisions about the correctness of answers.

2022-04-06

ArXiv (preprint)

Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation

Kushal Arora

Layla El Asri

Hareesh Bahuleyan

Current language generation models suffer from issues such as repetition, incoherence, and hallucinations. An often-repeated hypothesis for … (see more)this brittleness of generation models is that it is caused by the training and the generation procedure mismatch, also referred to as exposure bias. In this paper, we verify this hypothesis by analyzing exposure bias from an imitation learning perspective. We show that exposure bias leads to an accumulation of errors during generation, analyze why perplexity fails to capture this accumulation of errors, and empirically show that this accumulation results in poor generation quality.

2022-04-03

ArXiv (preprint)

Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences

Jad Kabbara

Presuppositions are assumptions that are taken for granted by an utterance, and identifying them is key to a pragmatic interpretation of lan… (see more)guage. In this paper, we investigate the capabilities of transformer models to perform NLI on cases involving presupposition. First, we present simple heuristics to create alternative “contrastive” test cases based on the ImpPres dataset and investigate the model performance on those test cases. Second, to better understand how the model is making its predictions, we analyze samples from sub-datasets of ImpPres and examine model performance on them. Overall, our findings suggest that NLI-trained transformer models seem to be exploiting specific structural and lexical cues as opposed to performing some kind of pragmatic reasoning.

2022-01-01

COLING (published)

dblp.uni-trier.de

Learning with Rejection for Abstractive Text Summarization

Meng Cao

Yue Dong

Jingyi He

2022-01-01

EMNLP (published)

Question Personalization in an Intelligent Tutoring System

Sabina Elkins

Robert Belfer

Ekaterina Kochmar

Iulian V. Serban

2022-01-01

AIED (2) (published)

Source-summary Entity Aggregation in Abstractive Summarization.

José-ángel González

Annie Priyadarshini Louis

2022-01-01

COLING (published)

dblp.uni-trier.de

Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge

Ian Porada

Alessandro Sordoni

Transformer models pre-trained with a masked-language-modeling objective (e.g., BERT) encode commonsense knowledge as evidenced by behaviora… (see more)l probes; however, the extent to which this knowledge is acquired by systematic inference over the semantics of the pre-training corpora is an open question. To answer this question, we selectively inject verbalized knowledge into the pre-training minibatches of BERT and evaluate how well the model generalizes to supported inferences after pre-training on the injected knowledge. We find generalization does not improve over the course of pre-training BERT from scratch, suggesting that commonsense knowledge is acquired from surface-level, co-occurrence patterns rather than induced, systematic reasoning.

2021-12-16

ArXiv (preprint)

The Topic Confusion Task: A Novel Evaluation Scenario for Authorship Attribution

Malik H. Altakrori

Benjamin Fung

2021-11-01

Findings of the Association for Computational Linguistics: EMNLP 2021 (published)

On-the-Fly Attention Modulation for Neural Generation

Yue Dong

Chandra Bhagavatula

Ximing Lu

Jena D. Hwang

Antoine Bosselut

Yejin Choi

Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from degeneration: the genera… (see more)ted text is repetitive, generic, self-contradictory, and often lacks commonsense. Our analyses on sentence-level attention patterns in LMs reveal that neural degeneration may be associated with insufficient learning of task-specific characteristics by the attention mechanism. This finding motivates on-the-fly attention modulation -- a simple but effective method that enables the injection of priors into attention computation during inference. Automatic and human evaluation results on three text generation benchmarks demonstrate that attention modulation helps LMs generate text with enhanced fluency, creativity, and commonsense reasoning, in addition to significantly reduce sentence-level repetition.

2021-08-01

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (published)