4 code implementations • ICLR 2021 • Hyung Won Chung, Thibault Févry, Henry Tsai, Melvin Johnson, Sebastian Ruder
We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models.
Ranked #1 on Cross-Lingual NER on NER
Cross-Lingual Natural Language Inference Cross-Lingual NER +4
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.
Ranked #15 on Entity Linking on AIDA-CoNLL
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.
no code implementations • 11 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.
Ranked #1 on Entity Linking on CoNLL-Aida
no code implementations • 1 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).
2 code implementations • 20 Mar 2019 • Nan Wu, Jason Phang, Jungkyu Park, Yiqiu Shen, Zhe Huang, Masha Zorin, Stanisław Jastrzębski, Thibault Févry, Joe Katsnelson, Eric Kim, Stacey Wolfson, Ujas Parikh, Sushma Gaddam, Leng Leng Young Lin, Kara Ho, Joshua D. Weinstein, Beatriu Reig, Yiming Gao, Hildegard Toth, Kristine Pysarenko, Alana Lewin, Jiyon Lee, Krystal Airola, Eralda Mema, Stephanie Chung, Esther Hwang, Naziya Samreen, S. Gene Kim, Laura Heacock, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200, 000 exams (over 1, 000, 000 images).
1 code implementation • 2 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.
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.