@inproceedings{poliak-etal-2018-collecting-diverse,
title = "Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation",
author = "Poliak, Adam and
Haldar, Aparajita and
Rudinger, Rachel and
Hu, J. Edward and
Pavlick, Ellie and
White, Aaron Steven and
Van Durme, Benjamin",
editor = "Linzen, Tal and
Chrupa{\l}a, Grzegorz and
Alishahi, Afra",
booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5441",
doi = "10.18653/v1/W18-5441",
pages = "337--340",
abstract = "We present a large scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation encoded by a neural network captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. Our collection of diverse datasets is available at \url{http://www.decomp.net/}, and will grow over time as additional resources are recast and added from novel sources.",
}
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<abstract>We present a large scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation encoded by a neural network captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. Our collection of diverse datasets is available at http://www.decomp.net/, and will grow over time as additional resources are recast and added from novel sources.</abstract>
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%0 Conference Proceedings
%T Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
%A Poliak, Adam
%A Haldar, Aparajita
%A Rudinger, Rachel
%A Hu, J. Edward
%A Pavlick, Ellie
%A White, Aaron Steven
%A Van Durme, Benjamin
%Y Linzen, Tal
%Y Chrupała, Grzegorz
%Y Alishahi, Afra
%S Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F poliak-etal-2018-collecting-diverse
%X We present a large scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation encoded by a neural network captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. Our collection of diverse datasets is available at http://www.decomp.net/, and will grow over time as additional resources are recast and added from novel sources.
%R 10.18653/v1/W18-5441
%U https://aclanthology.org/W18-5441
%U https://doi.org/10.18653/v1/W18-5441
%P 337-340
Markdown (Informal)
[Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation](https://aclanthology.org/W18-5441) (Poliak et al., EMNLP 2018)
ACL