Publications

BanditSum: Extractive Summarization as a Contextual Bandit

Yue Dong

Yikang Shen

Eric Crawford

Herke van Hoof

Jackie CK Cheung

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.

2018-09-30

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

HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Zhilin Yang

Peng Qi

Saizheng Zhang

William W. Cohen

Russ Salakhutdinov

Christopher D Manning

Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We int… (voir plus)roduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.

2018-09-30

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

Jackie CK Cheung

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.

2018-09-30

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

Introduction to NIPS 2017 Competition Track

Sergio Escalera

Markus Weimer

Mikhail Burtsev

Valentin Malykh

Varvara Logacheva

Ryan Lowe

Iulian V. Serban

Alexander Rudnicky

Alan W. Black

Shrimai Prabhumoye

Łukasz Kidziński

Sharada Prasanna Mohanty

Carmichael F. Ong

Jennifer L. Hicks

Sergey Levine

Marcel Salathé

Scott Delp

Iker Huerga

Alexander Grigorenko … (voir 19 de plus)

Leifur Thorbergsson

Anasuya Das

Kyla Nemitz

Jenna Sandker

Stephen King

Alexander S. Ecker

Leon A. Gatys

Matthias Bethge

Jordan Boyd-Graber

Shi Feng

Pedro Rodriguez

Mohit Iyyer

He He

Hal Daumé III

Sean McGregor

Amir Banifatemi

Alexey Kurakin

Ian G Goodfellow

Samy Bengio

2018-09-27

The NIPS '17 Competition: Building Intelligent Systems (publié)

The First Conversational Intelligence Challenge

Mikhail Burtsev

Varvara Logacheva

Valentin Malykh

Iulian V. Serban

Ryan Lowe

Shrimai Prabhumoye

Alan W. Black

Alexander Rudnicky

2018-09-27

The NIPS '17 Competition: Building Intelligent Systems (publié)

Combining adaptive algorithms and hypergradient method: a performance and robustness study

Akram Erraqabi

Nicolas Roux

2018-09-26

(publié)

Convergence Properties of Deep Neural Networks on Separable Data

Remi Tachet des Combes

Mohammad Pezeshki

Samira Shabanian

Aaron Courville

While a lot of progress has been made in recent years, the dynamics of learning in deep nonlinear neural networks remain to this day largely… (voir plus) misunderstood. In this work, we study the case of binary classification and prove various properties of learning in such networks under strong assumptions such as linear separability of the data. Extending existing results from the linear case, we confirm empirical observations by proving that the classification error also follows a sigmoidal shape in nonlinear architectures. We show that given proper initialization, learning expounds parallel independent modes and that certain regions of parameter space might lead to failed training. We also demonstrate that input norm and features’ frequency in the dataset lead to distinct convergence speeds which might shed some light on the generalization capabilities of deep neural networks. We provide a comparison between the dynamics of learning with cross-entropy and hinge losses, which could prove useful to understand recent progress in the training of generative adversarial networks. Finally, we identify a phenomenon that we baptize gradient starvation where the most frequent features in a dataset prevent the learning of other less frequent but equally informative features.

2018-09-26

(publié)

Deep Graph Infomax

Petar Veličković

William Fedus

William L. Hamilton

Pietro Lio

R Devon Hjelm

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised ma… (voir plus)nner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.

2018-09-26

ArXiv (prépublication)

DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation

Rim Assouel

Mohamed Ahmed

Marwin Segler

Amir Saffari

Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery. Recently, deep generative mo… (voir plus)dels have shown a promising way of performing de-novo molecular design. Although graph generative models are currently available they either have a graph size dependency in their number of parameters, limiting their use to only very small graphs or are formulated as a sequence of discrete actions needed to construct a graph, making the output graph non-differentiable w.r.t the model parameters, therefore preventing them to be used in scenarios such as conditional graph generation. In this work we propose a model for conditional graph generation that is computationally efficient and enables direct optimisation of the graph. We demonstrate favourable performance of our model on prototype-based molecular graph conditional generation tasks.

2018-09-26

ArXiv (prépublication)

On Difficulties of Probability Distillation

Alexandre Lacoste

2018-09-26

(publié)

Dopamine: A Research Framework for Deep Reinforcement Learning

Pablo Samuel Castro

Subhodeep Moitra

Carles Gelada

Saurabh Kumar

Bellemare Marc-Emmanuel

Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provid… (voir plus)e stable, comprehensive implementations for benchmarking. At the same time, recent deep RL research has become more diverse in its goals. In this paper we introduce Dopamine, a new research framework for deep RL that aims to support some of that diversity. Dopamine is open-source, TensorFlow-based, and provides compact and reliable implementations of some state-of-the-art deep RL agents. We complement this offering with a taxonomy of the different research objectives in deep RL research. While by no means exhaustive, our analysis highlights the heterogeneity of research in the field, and the value of frameworks such as ours.

2018-09-26

ArXiv (prépublication)