Publications

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)

arxiv.org

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

Ali Emami

Paul Trichelair

Adam Trischler

Kaheer Suleman

Hannes Schulz

Jackie Cheung

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)

doi.org

Automatic differentiation in ML: Where we are and where we should be going

Bart van Merriënboer

Olivier Breuleux

Arnaud Bergeron

Pascal Lamblin

We review the current state of automatic differentiation (AD) for array programming in machine learning (ML), including the different approa… (voir plus)ches such as operator overloading (OO) and source transformation (ST) used for AD, graph-based intermediate representations for programs, and source languages. Based on these insights, we introduce a new graph-based intermediate representation (IR) which specifically aims to efficiently support fully-general AD for array programming. Unlike existing dataflow programming representations in ML frameworks, our IR naturally supports function calls, higher-order functions and recursion, making ML models easier to implement. The ability to represent closures allows us to perform AD using ST without a tape, making the resulting derivative (adjoint) program amenable to ahead-of-time optimization using tools from functional language compilers, and enabling higher-order derivatives. Lastly, we introduce a proof of concept compiler toolchain called Myia which uses a subset of Python as a front end.

2018-10-01

ArXiv (prépublication)

arxiv.org

BanditSum: Extractive Summarization as a Contextual Bandit

Yue Dong

Yikang Shen

Eric Crawford

Herke van Hoof

Jackie 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.

2018-10-01

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

doi.org

arxiv.org

A Knowledge Hunting Framework for Common Sense Reasoning

Ali Emami

Noelia De La Cruz

Adam Trischler

Kaheer Suleman

Jackie 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.

2018-10-01

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

doi.org

arxiv.org

Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation

Tanya Nair

Doina Precup

Douglas Arnold

Tal Arbel

2018-09-26

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (publié)

doi.org