Jackie Cheung

Yu Bai

Research Intern - McGill University

PhD - McGill University

Aishik Chakraborty

PhD - McGill University

Khaoula Chehbouni

PhD - McGill University

Principal supervisor :

Master's Research - McGill University

Andre Cianflone

PhD - McGill University

Antoine Dangeard

Research Intern - McGill University

Maxime Darrin

PhD - McGill University

Co-supervisor :

Master's Research - McGill University

PhD - McGill University

Aylin Erman

PhD - McGill University

Co-supervisor :

Dan Poenaru

Ori Ernst

Postdoctorate - McGill University

Master's Research - McGill University

Jules Gagnon-marchand

Master's Research - McGill University

Sienna Hsu

Research Intern - McGill University University

Steven Koniaev

Research Intern - McGill University

Zichao Li

PhD - McGill University

Principal supervisor :

Siva Reddy

Yu Lu Liu

Master's Research - McGill University

Caleb Moses

PhD - McGill University

Martin Pömsl

Master's Research - McGill University

Ian Porada

PhD - McGill University

PhD - McGill University

Sina Salmannia

Undergraduate - McGill University

Cesare Spinoso-Di Piano

PhD - McGill University

Nathan Zeweniuk

Research Intern - McGill University University

Xiyuan Zou

Research Intern - McGill University

Publications

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

Zichao Li

Prakhar Sharma

Xing Han Lu

Siva Reddy

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

Zichao Li

Prakhar Sharma

Xing Han Lu

Siva Reddy

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)

Optimizing Deeper Transformers on Small Datasets

Peng Xu

Dhruv Kumar

Wei Yang

Wenjie Zi

Keyi Tang

Chenyang Huang

S. Prince

Yanshuai Cao

It is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually… (see more) use shallow and simple additional layers on top of pre-trained models during fine-tuning. This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension. In particular, we successfully train 48 layers of transformers, comprising 24 fine-tuned layers from pre-trained RoBERTa and 24 relation-aware layers trained from scratch. With fewer training steps and no task-specific pre-training, we obtain the state of the art performance on the challenging cross-domain Text-to-SQL parsing benchmark Spider. We achieve this by deriving a novel Data dependent Transformer Fixed-update initialization scheme (DT-Fixup), inspired by the prior T-Fixup work. Further error analysis shows that increasing depth can help improve generalization on small datasets for hard cases that require reasoning and structural understanding.

2021-08-01

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (published)

TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion

Jiapeng Wu

Yishi Xu

Yingxue Zhang

Chen Ma

Mark Coates

Reasoning in a temporal knowledge graph (TKG) is a critical task for information retrieval and semantic search. It is particularly challengi… (see more)ng when the TKG is updated frequently. The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge. Recent work approaches TKG completion (TKGC) by augmenting the encoder-decoder framework with a time-aware encoding function. However, naively fine-tuning the model at every time step using these methods does not address the problems of 1) catastrophic forgetting, 2) the model's inability to identify the change of facts (e.g., the change of the political affiliation and end of a marriage), and 3) the lack of training efficiency. To address these challenges, we present the Time-aware Incremental Embedding (TIE) framework, which combines TKG representation learning, experience replay, and temporal regularization. We introduce a set of metrics that characterizes the intransigence of the model and propose a constraint that associates the deleted facts with negative labels. Experimental results on Wikidata12k and YAGO11k datasets demonstrate that the proposed TIE framework reduces training time by about ten times and improves on the proposed metrics compared to vanilla full-batch training. It comes without a significant loss in performance for any traditional measures. Extensive ablation studies reveal performance trade-offs among different evaluation metrics, which is essential for decision-making around real-world TKG applications.

2021-07-11

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (published)