Jackie Cheung

Google Scholar

Yu Bai

Stagiaire de recherche - McGill

Doctorat - McGill

Doctorat - McGill

Doctorat - McGill

Superviseur⋅e principal⋅e :

Maîtrise recherche - McGill

Doctorat - McGill

Stagiaire de recherche - McGill

Maxime Darrin

Doctorat - McGill

Co-superviseur⋅e :

Maîtrise recherche - McGill

Doctorat - McGill

Aylin Erman

Doctorat - McGill

Co-superviseur⋅e :

Dan Poenaru

Ori Ernst

Postdoctorat - McGill

Maîtrise recherche - McGill

Google Scholar

Jules Gagnon-marchand

Maîtrise recherche - McGill

Sienna Hsu

Stagiaire de recherche - McGill University

Steven Koniaev

Stagiaire de recherche - McGill

Zichao Li

Doctorat - McGill

Superviseur⋅e principal⋅e :

Siva Reddy

Yu Lu Liu

Maîtrise recherche - McGill

Caleb Moses

Doctorat - McGill

Martin Pömsl

Maîtrise recherche - McGill

Google Scholar

Ian Porada

Doctorat - McGill

Doctorat - McGill

Baccalauréat - McGill

Cesare Spinoso-Di Piano

Doctorat - McGill

Nathan Zeweniuk

Stagiaire de recherche - McGill University

Xiyuan Zou

Stagiaire de recherche - McGill

Publications

Unsupervised Controllable Text Generation with Global Variation Discovery and Disentanglement

Peng Xu

Yanshuai Cao

Existing controllable text generation systems rely on annotated attributes, which greatly limits their capabilities and applications. In thi… (voir plus)s work, we make the first successful attempt to use VAEs to achieve controllable text generation without supervision. We do so by decomposing the latent space of the VAE into two parts: one incorporates structural constraints to capture dominant global variations implicitly present in the data, e.g., sentiment or topic; the other is unstructured and is used for the reconstruction of the source sentences. With the enforced structural constraint, the underlying global variations will be discovered and disentangled during the training of the VAE. The structural constraint also provides a natural recipe for mitigating posterior collapse for the structured part, which cannot be fully resolved by the existing techniques. On the task of text style transfer, our unsupervised approach achieves significantly better performance than previous supervised approaches. By showcasing generation with finer-grained control including Cards-Against-Humanity-style topic transitions within a sentence, we demonstrate that our model can perform controlled text generation in a more flexible way than existing methods.

2019-05-28

ArXiv (prépublication)

What comes next? Extractive summarization by next-sentence prediction

Jingyun Liu

Annie Priyadarshini Louis

Existing approaches to automatic summarization assume that a length limit for the summary is given, and view content selection as an optimiz… (voir plus)ation problem to maximize informativeness and minimize redundancy within this budget. This framework ignores the fact that human-written summaries have rich internal structure which can be exploited to train a summarization system. We present NEXTSUM, a novel approach to summarization based on a model that predicts the next sentence to include in the summary using not only the source article, but also the summary produced so far. We show that such a model successfully captures summary-specific discourse moves, and leads to better content selection performance, in addition to automatically predicting how long the target summary should be. We perform experiments on the New York Times Annotated Corpus of summaries, where NEXTSUM outperforms lead and content-model summarization baselines by significant margins. We also show that the lengths of summaries produced by our system correlates with the lengths of the human-written gold standards.

2019-01-12

ArXiv (prépublication)

Clustering-Oriented Representation Learning with Attractive-Repulsive Loss

Kian Kenyon-Dean

Andre Cianflone

Lucas Caccia

Guillaume Rabusseau

Doina Precup

The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the trai… (voir plus)ning data; building useful representations is not a necessary byproduct of this objective. In this work, we propose clustering-oriented representation learning (COREL) as an alternative to CCE in the context of a generalized attractive-repulsive loss framework. COREL has the consequence of building latent representations that collectively exhibit the quality of natural clustering within the latent space of the final hidden layer, according to a predefined similarity function. Despite being simple to implement, COREL variants outperform or perform equivalently to CCE in a variety of scenarios, including image and news article classification using both feed-forward and convolutional neural networks. Analysis of the latent spaces created with different similarity functions facilitates insights on the different use cases COREL variants can satisfy, where the Cosine-COREL variant makes a consistently clusterable latent space, while Gaussian-COREL consistently obtains better classification accuracy than CCE.

2018-12-18

ArXiv (prépublication)

Multi-task Learning over Graph Structures

Pengfei Liu

Jie Fu

Yue Dong

Xipeng Qiu

We present two architectures for multi-task learning with neural sequence models. Our approach allows the relationships between different ta… (voir plus)sks to be learned dynamically, rather than using an ad-hoc pre-defined structure as in previous work. We adopt the idea from message-passing graph neural networks and propose a general \textbf{graph multi-task learning} framework in which different tasks can communicate with each other in an effective and interpretable way. We conduct extensive experiments in text classification and sequence labeling to evaluate our approach on multi-task learning and transfer learning. The empirical results show that our models not only outperform competitive baselines but also learn interpretable and transferable patterns across tasks.

2018-11-26

ArXiv (prépublication)

On the Evaluation of Common-Sense Reasoning in Natural Language Understanding

Paul Trichelair

Ali Emami

Adam Trischler

Kaheer Suleman

Fernando Diaz

The NLP and ML communities have long been interested in developing models capable of common-sense reasoning, and recent works have significa… (voir plus)ntly improved the state of the art on benchmarks like the Winograd Schema Challenge (WSC). Despite these advances, the complexity of tasks designed to test common-sense reasoning remains under-analyzed. In this paper, we make a case study of the Winograd Schema Challenge and, based on two new measures of instance-level complexity, design a protocol that both clarifies and qualifies the results of previous work. Our protocol accounts for the WSC's limited size and variable instance difficulty, properties common to other common-sense benchmarks. Accounting for these properties when assessing model results may prevent unjustified conclusions.

2018-11-05

arXiv.org (prépublication)

dblp.uni-trier.de

The Hard-CoRe Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution

Ali Emami

Paul Trichelair

Adam Trischler

Kaheer Suleman

Hannes Schulz

We introduce a new benchmark task for coreference resolution, Hard-CoRe, that targets common-sense reasoning and world knowledge. Previous c… (voir plus)oreference resolution tasks have been overly vulnerable to systems that simply exploit the number and gender of the antecedents, or have been handcrafted and do not reflect the diversity of sentences in naturally occurring text. With these limitations in mind, we present a resolution task that is both challenging and realistic. We demonstrate that various coreference systems, whether rule-based, feature-rich, graphical, or neural-based, perform at random or slightly above-random on the task, whereas human performance is very strong with high inter-annotator agreement. To explain this performance gap, we show empirically that state-of-the art models often fail to capture context and rely only on the antecedents to make a decision.

2018-11-02

ArXiv (prépublication)

The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution

Ali Emami

Paul Trichelair

Adam Trischler

Kaheer Suleman

Hannes Schulz

We introduce a new benchmark for coreference resolution and NLI, KnowRef, that targets common-sense understanding and world knowledge. Previ… (voir plus)ous coreference resolution tasks can largely be solved by exploiting the number and gender of the antecedents, or have been handcrafted and do not reflect the diversity of naturally occurring text. We present a corpus of over 8,000 annotated text passages with ambiguous pronominal anaphora. These instances are both challenging and realistic. We show that various coreference systems, whether rule-based, feature-rich, or neural, perform significantly worse on the task than humans, who display high inter-annotator agreement. To explain this performance gap, we show empirically that state-of-the art models often fail to capture context, instead relying on the gender or number of candidate antecedents to make a decision. We then use problem-specific insights to propose a data-augmentation trick called antecedent switching to alleviate this tendency in models. Finally, we show that antecedent switching yields promising results on other tasks as well: we use it to achieve state-of-the-art results on the GAP coreference task.

2018-11-02

Annual Meeting of the Association for Computational Linguistics (publié)

BanditSum: Extractive Summarization as a Contextual Bandit

Yue Dong

Yikang Shen

Eric Crawford

Herke van Hoof

In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristical… (voir plus)ly-generated extractive labels. We call our approach BanditSum as it treats extractive summarization as a contextual bandit (CB) problem, where the model receives a document to summarize (the context), and chooses a sequence of sentences to include in the summary (the action). A policy gradient reinforcement learning algorithm is used to train the model to select sequences of sentences that maximize ROUGE score. We perform a series of experiments demonstrating that BanditSum is able to achieve ROUGE scores that are better than or comparable to the state-of-the-art for extractive summarization, and converges using significantly fewer update steps than competing approaches. In addition, we show empirically that BanditSum performs significantly better than competing approaches when good summary sentences appear late in the source document.

2018-10-01

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (publié)

A Knowledge Hunting Framework for Common Sense Reasoning

Ali Emami

Noelia De La Cruz

Adam Trischler

Kaheer Suleman

We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning tas… (voir plus)k that requires diverse, complex forms of inference and knowledge. Our method uses a knowledge hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine, then extracts and classifies knowledge from the returned results and weighs them to make a resolution. Our approach improves F1 performance on the full WSC by 0.21 over the previous best and represents the first system to exceed 0.5 F1. We further demonstrate that the approach is competitive on the Choice of Plausible Alternatives (COPA) task, which suggests that it is generally applicable.

2018-10-01

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (publié)

Let’s do it “again”: A First Computational Approach to Detecting Adverbial Presupposition Triggers

Andre Cianflone

Yulan Feng

Jad Kabbara

We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as s… (voir plus)ummarization and dialogue systems. We introduce two new corpora, derived from the Penn Treebank and the Annotated English Gigaword dataset and investigate the use of a novel attention mechanism tailored to this task. Our attention mechanism augments a baseline recurrent neural network without the need for additional trainable parameters, minimizing the added computational cost of our mechanism. We demonstrate that this model statistically outperforms our baselines.

2018-07-01

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)

Coarse Lexical Frame Acquisition at the Syntax–Semantics Interface Using a Latent-Variable PCFG Model

Laura Kallmeyer

Behrang QasemiZadeh

We present a method for unsupervised lexical frame acquisition at the syntax–semantics interface. Given a set of input strings derived fro… (voir plus)m dependency parses, our method generates a set of clusters that resemble lexical frame structures. Our work is motivated not only by its practical applications (e.g., to build, or expand the coverage of lexical frame databases), but also to gain linguistic insight into frame structures with respect to lexical distributions in relation to grammatical structures. We model our task using a hierarchical Bayesian network and employ tools and methods from latent variable probabilistic context free grammars (L-PCFGs) for statistical inference and parameter fitting, for which we propose a new split and merge procedure. We show that our model outperforms several baselines on a portion of the Wall Street Journal sentences that we have newly annotated for evaluation purposes.

2018-06-01

Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics (publié)

Commonsense mining as knowledge base completion? A study on the impact of novelty

Stanisław Jastrzębski

Dzmitry Bahdanau

Seyedarian Hosseini

Michael Noukhovitch

Yoshua Bengio

Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples. Inspired by recent work by Li et al., … (voir plus)we analyse if knowledge base completion models can be used to mine commonsense knowledge from raw text. We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method that outperforms the previous state of the art on predicting more novel triples.

2018-06-01

Proceedings of the Workshop on Generalization in the Age of Deep Learning (publié)