Bang Liu

Google Scholar

Haochen Shi

PhD - Université de Montréal

haochen.shi@mila.quebec

Jeremy Qin

Master's Research - Université de Montréal

jeremy.qin@mila.quebec

Jinghan Sun

Master's Research - Université de Montréal

jinghan.sun@mila.quebec

Website

qianggang.ding@mila.quebec

Google Scholar

Kyle Roth

PhD - Université de Montréal

kyle.roth@mila.quebec

Website

Qianggang Ding

PhD - Université de Montréal

Rushil Gupta

Master's Research - Université de Montréal

rushil.gupta@mila.quebec

suyuchen.wang@mila.quebec

Google Scholar

Sifan Wu

PhD - Université de Montréal

sifan.wu@mila.quebec

Suyuchen Wang

PhD - Université de Montréal

xiaoqiang.wang@mila.quebec

Tony Yuan

Master's Research - Université de Montréal

tony.yuan@mila.quebec

Xiaoqiang Wang

PhD - Université de Montréal

Xiaotong Lyu

Research Intern - Université de Montréal

xiaotong.lyu@mila.quebec

Yizhan Li

Master's Research - Université de Montréal

yizhan.li@mila.quebec

Zhiyuan Sun

Master's Research - Université de Montréal

zhiyuan.sun@mila.quebec

Publications

GOAt: Explaining Graph Neural Networks via Graph Output Attribution

Shengyao Lu

Keith G Mills

Jiao He

Di Niu

Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability. Most existing methods for ex… (see more)plaining GNNs typically rely on training auxiliary models, resulting in the explanations remain black-boxed. This paper introduces Graph Output Attribution (GOAt), a novel method to attribute graph outputs to input graph features, creating GNN explanations that are faithful, discriminative, as well as stable across similar samples. By expanding the GNN as a sum of scalar products involving node features, edge features and activation patterns, we propose an efficient analytical method to compute contribution of each node or edge feature to each scalar product and aggregate the contributions from all scalar products in the expansion form to derive the importance of each node and edge. Through extensive experiments on synthetic and real-world data, we show that our method not only outperforms various state-of-the-art GNN explainers in terms of the commonly used fidelity metric, but also exhibits stronger discriminability, and stability by a remarkable margin.

2024-01-16

ICLR.cc/2024/Conference (poster)

Efficient Classification of Long Documents via State-Space Models

Peng Lu

Suyuchen Wang

Mehdi Rezagholizadeh

Ivan Kobyzev

2023-10-07

EMNLP/2023/Conference (accepted)

HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science

Yu Song

Santiago Miret

Huan Zhang

2023-10-07

EMNLP/2023/Conference (published)

MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization

Yuyan Chen

Zhihao Wen

Ge Fan

Zhengyu Chen

Wei Wu

Dayiheng Liu

Zhixu Li

Yanghua Xiao

2023-10-07

EMNLP/2023/Conference (published)

SkillQG: Learning to Generate Question for Reading Comprehension Assessment

Xiaoqiang Wang

Siliang Tang

Lingfei Wu

2023-07-01

Findings of the Association for Computational Linguistics: ACL 2023 (published)

Fine-tuning Happens in Tiny Subspaces: Exploring Intrinsic Task-specific Subspaces of Pre-trained Language Models

Zhong Zhang

Junming Shao

2023-05-27

ArXiv (preprint)

MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling

Yurun Song

Santiago Miret

2023-05-14

ArXiv (preprint)

QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance

Xiaoqiang Wang

Siliang Tang

Lingfei Wu

Existing metrics for assessing question generation not only require costly human reference but also fail to take into account the input cont… (see more)ext of generation, rendering the lack of deep understanding of the relevance between the generated questions and input contexts. As a result, they may wrongly penalize a legitimate and reasonable candidate question when it (1) involves complicated reasoning with the context or (2) can be grounded by multiple evidences in the context.In this paper, we propose QRelScore, a context-aware Relevance evaluation metric for Question Generation.Based on off-the-shelf language models such as BERT and GPT2, QRelScore employs both word-level hierarchical matching and sentence-level prompt-based generation to cope with the complicated reasoning and diverse generation from multiple evidences, respectively.Compared with existing metrics, our experiments demonstrate that QRelScore is able to achieve a higher correlation with human judgments while being much more robust to adversarial samples.

2022-12-01

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (published)

Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning

Martin Weyssow

Houari Sahraoui

The progress made in code modeling has been tremendous in recent years thanks to the design of natural language processing learning approach… (see more)es based on state-of-the-art model architectures. Nevertheless, we believe that the current state-of-the-art does not focus enough on the full potential that data may bring to a learning process in software engineering. Our vision articulates on the idea of leveraging multi-modal learning approaches to modeling the programming world. In this paper, we investigate one of the underlying idea of our vision whose objective based on concept graphs of identifiers aims at leveraging high-level relationships between domain concepts manipulated through particular language constructs. In particular, we propose to enhance an existing pretrained language model of code by joint-learning it with a graph neural network based on our concept graphs. We conducted a preliminary evaluation that shows gain of effectiveness of the models for code search using a simple joint-learning method and prompts us to further investigate our research vision.

2022-05-22

2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) (published)

Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation

Yuyan Chen

Yanghua Xiao

Interpreting the predictions of existing Question Answering (QA) models is critical to many real-world intelligent applications, such as QA … (see more)systems for healthcare, education, and finance. However, existing QA models lack interpretability and provide no feedback or explanation for end-users to help them understand why a specific prediction is the answer to a question. In this research, we argue that the evidences of an answer is critical to enhancing the interpretability of QA models. Unlike previous research that simply extracts several sentence(s) in the context as evidence, we are the first to explicitly define the concept of evidence as the supporting facts in a context which are informative, concise, and readable. Besides, we provide effective strategies to quantitatively measure the informativeness, conciseness and readability of evidence. Furthermore, we propose Grow-and-Clip Evidence Distillation (GCED) algorithm to extract evidences from the contexts by trade-off informativeness, conciseness, and readability. We conduct extensive experiments on the SQuAD and TriviaQA datasets with several baseline models to evaluate the effect of GCED on interpreting answers to questions. Human evaluation are also carried out to check the quality of distilled evidences. Experimental results show that automatic distilled evidences have human-like informativeness, conciseness and readability, which can enhance the interpretability of the answers to questions.

2022-05-09

2022 IEEE 38th International Conference on Data Engineering (ICDE) (published)

Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation

Jiayuan Ding

Tong Xiang

Zijing Ou

Wangyang Zuo

Ruihui Zhao

Chenhua Lin

Yefeng Zheng

Recent years have witnessed the dramatic growth of paper volumes with plenty of new research papers published every day, especially in the a… (see more)rea of computer science. How to glean papers worth reading from the massive literature to do a quick survey or keep up with the latest advancement about a specific research topic has become a challenging task. Existing academic search engines return relevant papers by individually calculating the relevance between each paper and query. However, such systems usually omit the prerequisite chains of a research topic and cannot form a meaningful reading path. In this paper, we introduce a new task named Reading Path Generation (RPG) which aims at automatically producing a path of papers to read for a given query. To serve as a research benchmark, we further propose SurveyBank, a dataset consisting of large quantities of survey papers in the field of computer science as well as their citation relationships. Furthermore, we propose a graph-optimization-based approach for reading path generation which takes the relationship between papers into account. Extensive evaluations demonstrate that our approach outperforms other baselines. A real-time Reading Path Generation (RePaGer) system has been also implemented with our designed model. Our source code and SurveyBank dataset can be found here11https://github.com/JiayuanDing100/Reading-Path-Generation.

2022-05-09

2022 IEEE 38th International Conference on Data Engineering (ICDE) (published)

Accepted Tutorials at The Web Conference 2022

Riccardo Tommasini

Senjuti Basu Roy

Xuan Wang

Hongwei Wang

Heng Ji

Jiawei Han

Preslav Nakov

Giovanni Da San Martino

Firoj Alam

Markus Schedl

Elisabeth Lex

Akash Bharadwaj

Graham Cormode

Milan Dojchinovski

Jan Forberg

Johannes Frey

Pieter Bonte

Marco Balduini

Matteo Belcao

Emanuele Della Valle … (see 53 more)

Junliang Yu

Hongzhi Yin

Tong Chen

Haochen Liu

Yiqi Wang

Wenqi Fan

Xiaorui Liu

Jamell Dacon

Lingjuan Lye

Jiliang Tang

Aristides Gionis

Stefan Neumann

Bruno Ordozgoiti

Simon Razniewski

Hiba Arnaout

Shrestha Ghosh

Fabian Suchanek

Lingfei Wu

Yu Chen

Yunyao Li

Filip Ilievski

Daniel Garijo

Hans Chalupsky

Pedro Szekely

Ilias Kanellos

Dimitris Sacharidis

Thanasis Vergoulis

Nurendra Choudhary

Nikhil Rao

Karthik Subbian

Srinivasan Sengamedu

Chandan K. Reddy

Friedhelm Victor

Bernhard Haslhofer

George Katsogiannis- Meimarakis

Georgia Koutrika

Shengmin Jin

Danai Koutra

Reza Zafarani

Yulia Tsvetkov

Vidhisha Balachandran

Sachin Kumar

Xiangyu Zhao

Bo Chen

Huifeng Guo

Yejing Wang

Ruiming Tang

Yang Zhang

Wenjie Wang

Peng Wu

Fuli Feng

Xiangnan He

This paper summarizes the content of the 20 tutorials that have been given at The Web Conference 2022: 85% of these tutorials are lecture st… (see more)yle, and 15% of these are hands on.

2022-04-25

The Web Conference (published)