@inproceedings{white-etal-2018-lexicosyntactic,
title = "Lexicosyntactic Inference in Neural Models",
author = "White, Aaron Steven and
Rudinger, Rachel and
Rawlins, Kyle 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/D18-1501",
doi = "10.18653/v1/D18-1501",
pages = "4717--4724",
abstract = "We investigate neural models{'} ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.",
}
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<abstract>We investigate neural models’ ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.</abstract>
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%0 Conference Proceedings
%T Lexicosyntactic Inference in Neural Models
%A White, Aaron Steven
%A Rudinger, Rachel
%A Rawlins, Kyle
%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 white-etal-2018-lexicosyntactic
%X We investigate neural models’ ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.
%R 10.18653/v1/D18-1501
%U https://aclanthology.org/D18-1501
%U https://doi.org/10.18653/v1/D18-1501
%P 4717-4724
Markdown (Informal)
[Lexicosyntactic Inference in Neural Models](https://aclanthology.org/D18-1501) (White et al., EMNLP 2018)
ACL
- Aaron Steven White, Rachel Rudinger, Kyle Rawlins, and Benjamin Van Durme. 2018. Lexicosyntactic Inference in Neural Models. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4717–4724, Brussels, Belgium. Association for Computational Linguistics.