@inproceedings{poliak-etal-2018-collecting,
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 = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1007",
doi = "10.18653/v1/D18-1007",
pages = "67--81",
abstract = "We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation 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. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at \url{https://www.decomp.net}, and will grow over time as additional resources are recast and added from novel sources.",
}
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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 Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F poliak-etal-2018-collecting
%X We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation 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. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at https://www.decomp.net, and will grow over time as additional resources are recast and added from novel sources.
%R 10.18653/v1/D18-1007
%U https://aclanthology.org/D18-1007
%U https://doi.org/10.18653/v1/D18-1007
%P 67-81
Markdown (Informal)
[Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation](https://aclanthology.org/D18-1007) (Poliak et al., EMNLP 2018)
ACL