@inproceedings{White2018,
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.},
address = {Stroudsburg, PA, USA},
archivePrefix = {arXiv},
arxivId = {1808.06232},
author = {White, Aaron Steven and Rudinger, Rachel and Rawlins, Kyle and {Van Durme}, Benjamin},
booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
doi = {10.18653/v1/D18-1501},
eprint = {1808.06232},
file = {:Users/shanest/Documents/Library/White et al/Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing/White et al. - 2018 - Lexicosyntactic Inference in Neural Models.pdf:pdf},
keywords = {dataset,phenomenon: factuality},
pages = {4717--4724},
publisher = {Association for Computational Linguistics},
title = {{Lexicosyntactic Inference in Neural Models}},
url = {https://www.aclweb.org/anthology/D18-1501/},
year = {2018}
}

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