Publications

Elucidating Transcriptional Networks Driving Idiopathic Pulmonary Fibrosis
O. Lapohos
M. El-Hajjar
E.S. Charette
A. Emad
G. Fonseca
2022-04-30
Varied Omics Techniques Applied to Allergic and Respiratory Traits (published)
doi.org
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
Nicholas Meade
Elinor Poole-Dayan
Siva Reddy
Recent work has shown pre-trained language models capture social biases from the large amounts of text they are trained on. This has attract… (see more)ed attention to developing techniques that mitigate such biases. In this work, we perform an empirical survey of five recently proposed bias mitigation techniques: Counterfactual Data Augmentation (CDA), Dropout, Iterative Nullspace Projection, Self-Debias, and SentenceDebias. We quantify the effectiveness of each technique using three intrinsic bias benchmarks while also measuring the impact of these techniques on a model’s language modeling ability, as well as its performance on downstream NLU tasks. We experimentally find that: (1) Self-Debias is the strongest debiasing technique, obtaining improved scores on all bias benchmarks; (2) Current debiasing techniques perform less consistently when mitigating non-gender biases; And (3) improvements on bias benchmarks such as StereoSet and CrowS-Pairs by using debiasing strategies are often accompanied by a decrease in language modeling ability, making it difficult to determine whether the bias mitigation was effective.
2022-04-30
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (published)
doi.org
arxiv.org
Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking
Yifei Li
Pratheeksha Nair
Kellin Pelrine
Reihaneh Rabbany
2022-04-30
Findings of the Association for Computational Linguistics: ACL 2022 (published)
doi.org
Feeding What You Need by Understanding What You Learned
Xiaoqiang Wang
Bang Liu
Fangli Xu
Bo Long
Siliang Tang
Lingfei Wu
2022-04-30
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (published)
doi.org
arxiv.org
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization
Meng Cao
Yue Dong
Jackie Chi Kit Cheung
State-of-the-art abstractive summarization systems often generate hallucinations; i.e., content that is not directly inferable from the sour… (see more)ce text. Despite being assumed to be incorrect, we find that much hallucinated content is actually consistent with world knowledge, which we call factual hallucinations. Including these factual hallucinations in a summary can be beneficial because they provide useful background information. In this work, we propose a novel detection approach that separates factual from non-factual hallucinations of entities. Our method is based on an entity’s prior and posterior probabilities according to pre-trained and finetuned masked language models, respectively. Empirical results suggest that our method vastly outperforms two baselines in both accuracy and F1 scores and has a strong correlation with human judgments on factuality classification tasks.Furthermore, we use our method as a reward signal to train a summarization system using an off-line reinforcement learning (RL) algorithm that can significantly improve the factuality of generated summaries while maintaining the level of abstractiveness.
2022-04-30
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (published)
doi.org
arxiv.org
Local Structure Matters Most: Perturbation Study in NLU
Louis Clouatre
Prasanna Parthasarathi
Amal Zouaq
Sarath Chandar
Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations has shown that neural models … (see more)are surprisingly insensitive to the order of words. In this paper, we investigate this phenomenon by developing order-altering perturbations on the order of words, subwords, and characters to analyze their effect on neural models' performance on language understanding tasks. We experiment with measuring the impact of perturbations to the local neighborhood of characters and global position of characters in the perturbed texts and observe that perturbation functions found in prior literature only affect the global ordering while the local ordering remains relatively unperturbed. We empirically show that neural models, invariant of their inductive biases, pretraining scheme, or the choice of tokenization, mostly rely on the local structure of text to build understanding and make limited use of the global structure.
2022-04-30
Findings of the Association for Computational Linguistics: ACL 2022 (published)
doi.org
arxiv.org
Neurobiological Correlates of Change in Adaptive Behavior in Autism.
Charlotte M. Pretzsch
Tim Schäfer
Michael V. Lombardo
Varun Warrier
Caroline Mann
Anke Bletsch
Chris H. Chatham
Dorothea L. Floris
Julian Tillmann
Afsheen Yousaf
Emily J. H. Jones
Tony Charman
Sara Ambrosino
Thomas Bourgeron
Guillaume Dumas
Eva Loth
Beth Oakley
Jan K. Buitelaar
Freddy Cliquet
Claire Leblond … (see 7 more)
Simon Baron-Cohen
Christian Beckmann
Tobias Banaschewski
Sarah Durston
Christine M. Freitag
Declan Murphy
Christine Ecker
2022-04-30
American Journal of Psychiatry (published)
doi.org
P397. Genomic Deletions and Duplications Show Mirror Effects on Cognitive Ability According to Spatial Patterns of Gene Expression in the Human Brain
Kuldeep Kumar
Sayeh Kazem
Guillaume Huguet
Élise Douard
Zohra Saci
Laura Almasy
David Glahn
Guillaume Dumas
Sébastien Jacquemont
2022-04-30
Biological Psychiatry (published)
doi.org
Performance-gated deliberation: A context-adapted strategy in which urgency is opportunity cost
Maximilian Puelma Touzel
Paul Cisek
Guillaume Lajoie
Finding the right amount of deliberation, between insufficient and excessive, is a hard decision making problem that depends on the value we… (see more) place on our time. Average-reward, putatively encoded by tonic dopamine, serves in existing reinforcement learning theory as the opportunity cost of time, including deliberation time. Importantly, this cost can itself vary with the environmental context and is not trivial to estimate. Here, we propose how the opportunity cost of deliberation can be estimated adaptively on multiple timescales to account for non-stationary contextual factors. We use it in a simple decision-making heuristic based on average-reward reinforcement learning (AR-RL) that we call Performance-Gated Deliberation (PGD). We propose PGD as a strategy used by animals wherein deliberation cost is implemented directly as urgency, a previously characterized neural signal effectively controlling the speed of the decision-making process. We show PGD outperforms AR-RL solutions in explaining behaviour and urgency of non-human primates in a context-varying random walk prediction task and is consistent with relative performance and urgency in a context-varying random dot motion task. We make readily testable predictions for both neural activity and behaviour.
2022-04-30
PLoS Comput. Biol. (published)
doi.org
Predicting the probability distribution of bus travel time to measure the reliability of public transport services
Léa Ricard
Guy Desaulniers
Andrea Lodi
Louis-Martin Rousseau
2022-04-30
Transportation Research Part C: Emerging Technologies (published)
doi.org
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering
Jing Zhang
Xiaokang Zhang
Jifan Yu
Jian Tang
Jie Tang
Cuiping Li
Hong Chen
Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. A desired subgraph is crucial as a small o… (see more)ne may exclude the answer but a large one might introduce more noises. However, the existing retrieval is either heuristic or interwoven with the reasoning, causing reasoning on the partial subgraphs, which increases the reasoning bias when the intermediate supervision is missing. This paper proposes a trainable subgraph retriever (SR) decoupled from the subsequent reasoning process, which enables a plug-and-play framework to enhance any subgraph-oriented KBQA model. Extensive experiments demonstrate SR achieves significantly better retrieval and QA performance than existing retrieval methods. Via weakly supervised pre-training as well as the end-to-end fine-tuning, SRl achieves new state-of-the-art performance when combined with NSM, a subgraph-oriented reasoner, for embedding-based KBQA methods.
2022-04-30
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (published)
doi.org
arxiv.org
On the estimation of discrete choice models to capture irrational customer behaviors
Sanjay Dominik Jena
Andrea Lodi
Claudio Sole
The random utility maximization model is by far the most adopted framework to estimate consumer choice behavior. However, behavioral economi… (see more)cs has provided strong empirical evidence of irrational choice behaviors, such as halo effects, that are incompatible with this framework. Models belonging to the random utility maximization family may therefore not accurately capture such irrational behavior. Hence, more general choice models, overcoming such limitations, have been proposed. However, the flexibility of such models comes at the price of increased risk of overfitting. As such, estimating such models remains a challenge. In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. In particular, we propose a column-generation method to gradually refine the discrete choice model based on partially ranked preference sequences. Extensive computational experiments indicate that our model, explicitly accounting for irrational preferences, can significantly boost the predictive accuracy on both synthetic and real-world data instances. Summary of Contribution: In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. Specifically, we show how to use partially ranked preferences to efficiently model rational and irrational customer types from transaction data. Our estimation procedure is based on column generation, where relevant customer types are efficiently extracted by expanding a treelike data structure containing the customer behaviors. Furthermore, we propose a new dominance rule among customer types whose effect is to prioritize low orders of interactions among products. An extensive set of experiments assesses the predictive accuracy of the proposed approach by comparing it against rank-based methods with only rational preferences and with more general benchmarks from the literature. Our results show that accounting for irrational preferences can boost predictive accuracy by 12.5% on average when tested on a real-world data set from a large chain of grocery and drug stores.
2022-04-30
INFORMS Journal on Computing (published)
doi.org
arxiv.org