Search Results for author: Thibault Févry

Found 8 papers, 5 papers with code

Empirical Evaluation of Pretraining Strategies for Supervised Entity Linking

no code implementations AKBC 2020 Thibault Févry, Nicholas FitzGerald, Livio Baldini Soares, Tom Kwiatkowski

In this work, we present an entity linking model which combines a Transformer architecture with large scale pretraining from Wikipedia links.

Entity Linking

Entities as Experts: Sparse Memory Access with Entity Supervision

1 code implementation EMNLP 2020 Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald, Eunsol Choi, Tom Kwiatkowski

We introduce a new model - Entities as Experts (EAE) - that can access distinct memories of the entities mentioned in a piece of text.

Language Modelling TriviaQA

Learning Cross-Context Entity Representations from Text

no code implementations11 Jan 2020 Jeffrey Ling, Nicholas FitzGerald, Zifei Shan, Livio Baldini Soares, Thibault Févry, David Weiss, Tom Kwiatkowski

Language modeling tasks, in which words, or word-pieces, are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases.

Entity Linking Language Modelling +2

Improving localization-based approaches for breast cancer screening exam classification

no code implementations1 Aug 2019 Thibault Févry, Jason Phang, Nan Wu, S. Gene Kim, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

We trained and evaluated a localization-based deep CNN for breast cancer screening exam classification on over 200, 000 exams (over 1, 000, 000 images).

Classification General Classification

Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks

2 code implementations2 Nov 2018 Jason Phang, Thibault Févry, Samuel R. Bowman

Pretraining sentence encoders with language modeling and related unsupervised tasks has recently been shown to be very effective for language understanding tasks.

Language Modelling Natural Language Inference +2

Unsupervised Sentence Compression using Denoising Auto-Encoders

1 code implementation CONLL 2018 Thibault Févry, Jason Phang

In sentence compression, the task of shortening sentences while retaining the original meaning, models tend to be trained on large corpora containing pairs of verbose and compressed sentences.

Denoising Sentence +1

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