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Adina Williams

Facebook AI Research

26 papers hereh-index 229.8k citations37 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author6
  • middle author13
  • last author7

Across the 26 of 26 papers where every author was matched, so the position is known.

fields
  • cs.CL25
  • cs.LG1
affiliations
  • Facebook AI Research
ORCID 0000-0001-5281-3343
same name
  • Adina Williams — 17 papers, h 15
  • Adina Williams — 9 papers, h 6
  • Adina Williams — 6 papers, h 3
  • Adina Williams — 5 papers, h 3
  • Adina Williams — 4 papers, h 5
  • Adina Williams — 4 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172023
most citedXNLI: Evaluating Cross-lingual Sentence Representations

148 citations · 272 across the 17 of their papers we have counts for

collaborators
Showing 2017Show all

3 papers · 1 filter

cs.CL2017★ 6 cited

Do latent tree learning models identify meaningful structure in sentences?

Adina Williams, Andrew Drozdov, Samuel R. Bowman

Recent work on the problem of latent tree learning has made it possible to train neural networks that learn to both parse a sentence and use the resulting parse to interpret the se…

cs.CL2017

The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations

Nikita Nangia, Adina Williams, Angeliki Lazaridou +1

This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inferenc…

cs.CL2017

A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

Adina Williams, Nikita Nangia, Samuel R. Bowman

This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sent…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.