Publications

Capacity Variation in the Many-to-one Stable Matching
Federico Bobbio
Margarida Carvalho
Andrea Lodi
Alfredo Torrico
2022-05-02
ArXiv (prépublication)
doi.org
arxiv.org
Deep Learning Prediction of Response to Disease Modifying Therapy in Primary Progressive Multiple Sclerosis (P1-1.Virtual)
Jean-Pierre René Falet
Joshua Durso-finley
Brennan Nichyporuk
Julien Schroeter
Francesca Bovis
Maria-Pia Sormani
Doina Precup
Tal Arbel
Douglas Arnold
2022-05-02
Neurology (inconnu)
doi.org
A general class of surrogate functions for stable and efficient reinforcement learning
Sharan Vaswani
Olivier Bachem
Simone Totaro
Robert Müller
Shivam Garg
Matthieu Geist
Marlos C. Machado
Pablo Samuel Castro
Nicolas Le Roux
Common policy gradient methods rely on the maximization of a sequence of surrogate functions. In recent years, many such surrogate functions… (voir plus) have been proposed, most without strong theoretical guarantees, leading to algorithms such as TRPO, PPO or MPO. Rather than design yet another surrogate function, we instead propose a general framework (FMA-PG) based on functional mirror ascent that gives rise to an entire family of surrogate functions. We construct surrogate functions that enable policy improvement guarantees, a property not shared by most existing surrogate functions. Crucially, these guarantees hold regardless of the choice of policy parameterization. Moreover, a particular instantiation of FMA-PG recovers important implementation heuristics (e.g., using forward vs reverse KL divergence) resulting in a variant of TRPO with additional desirable properties. Via experiments on simple bandit problems, we evaluate the algorithms instantiated by FMA-PG. The proposed framework also suggests an improved variant of PPO, whose robustness and efficiency we empirically demonstrate on the MuJoCo suite.
2022-05-02
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
proceedings.mlr.press
BDD-based optimization for the quadratic stable set problem
Jaime E. González
Andr'e Augusto Cire
Andrea Lodi
Louis-Martin Rousseau
2022-04-30
Discrete Optimization (publié)
doi.org
Characterizing Idioms: Conventionality and Contingency
Michaela Socolof
Jackie Chi Kit Cheung
Michael Wagner
Timothy J. O'Donnell
Idioms are unlike most phrases in two important ways. First, the words in an idiom have non-canonical meanings. Second, the non-canonical me… (voir plus)anings 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 implement them using BERT (Devlin et al., 2019) and XLNet(Yang et al., 2019). 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 special machinery to handle idioms may not be warranted.
2022-04-30
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)
doi.org
arxiv.org
Compositional Generalization in Dependency Parsing
Emily Goodwin
Siva Reddy
Timothy J. O'Donnell
Dzmitry Bahdanau
Compositionality -- the ability to combine familiar units like words into novel phrases and sentences -- has been the focus of intense inter… (voir plus)est in artificial intelligence in recent years. To test compositional generalization in semantic parsing, Keysers et al. (2020) introduced Compositional Freebase Queries (CFQ). This dataset maximizes the similarity between the test and train distributions over primitive units, like words, while maximizing the compound divergence: the dissimilarity between test and train distributions over larger structures, like phrases. Dependency parsing, however, lacks a compositional generalization benchmark. In this work, we introduce a gold-standard set of dependency parses for CFQ, and use this to analyze the behavior of a state-of-the art dependency parser (Qi et al., 2020) on the CFQ dataset. We find that increasing compound divergence degrades dependency parsing performance, although not as dramatically as semantic parsing performance. Additionally, we find the performance of the dependency parser does not uniformly degrade relative to compound divergence, and the parser performs differently on different splits with the same compound divergence. We explore a number of hypotheses for what causes the non-uniform degradation in dependency parsing performance, and identify a number of syntactic structures that drive the dependency parser's lower performance on the most challenging splits.
2022-04-30
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)
doi.org
arxiv.org
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 (publié)
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… (voir plus)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) (publié)
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 (publié)
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) (publié)
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… (voir plus)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) (publié)
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 … (voir plus)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 (publié)
doi.org
arxiv.org