A
Adina Williams
Researcher at Facebook
Publications - 73
Citations - 6859
Adina Williams is an academic researcher from Facebook. The author has contributed to research in topics: Computer science & Sentence. The author has an hindex of 20, co-authored 58 publications receiving 4032 citations. Previous affiliations of Adina Williams include New York University.
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Proceedings ArticleDOI
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
TL;DR: The Multi-Genre Natural Language Inference corpus is introduced, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding and shows that it represents a substantially more difficult task than does the Stanford NLI corpus.
Proceedings ArticleDOI
XNLI: Evaluating Cross-lingual Sentence Representations
Alexis Conneau,Ruty Rinott,Guillaume Lample,Adina Williams,Samuel R. Bowman,Holger Schwenk,Veselin Stoyanov +6 more
TL;DR: This work constructs an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus to 14 languages, including low-resource languages such as Swahili and Urdu and finds that XNLI represents a practical and challenging evaluation suite and that directly translating the test data yields the best performance among available baselines.
Proceedings ArticleDOI
Adversarial NLI: A New Benchmark for Natural Language Understanding
TL;DR: This work introduces a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure, and shows that non-expert annotators are successful at finding their weaknesses.
Posted Content
XNLI: Evaluating Cross-lingual Sentence Representations
Alexis Conneau,Guillaume Lample,Ruty Rinott,Adina Williams,Samuel R. Bowman,Holger Schwenk,Veselin Stoyanov +6 more
TL;DR: The authors constructed an evaluation set for cross-lingual language understanding by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15 languages, including low-resource languages such as Swahili and Urdu.
Posted Content
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
TL;DR: The Multi-Genre Natural Language Inference (MultiNLI) corpus as mentioned in this paper is a dataset designed for use in the development and evaluation of machine learning models for sentence understanding.