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arXiv · 2306.01966

GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation

Tatsuya Aoyama·Shabnam Behzad·Luke Gessler·Lauren Levine·Jessica Lin·Yang Janet Liu·Siyao Peng·Yilun Zhu·Amir Zeldes
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Abstract

We present GENTLE, a new mixed-genre English challenge corpus totaling 17K tokens and consisting of 8 unusual text types for out-of domain evaluation: dictionary entries, esports commentaries, legal documents, medical notes, poetry, mathematical proofs, syllabuses, and threat letters. GENTLE is manually annotated for a variety of popular NLP tasks, including syntactic dependency parsing, entity recognition, coreference resolution, and discourse parsing. We evaluate state-of-the-art NLP systems on GENTLE and find severe degradation for at least some genres in their performance on all tasks, which indicates GENTLE's utility as an evaluation dataset for NLP systems.

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Tatsuya Aoyama, Shabnam Behzad, Luke Gessler, Lauren Levine, Jessica Lin, Yang Janet Liu, Siyao Peng, Yilun Zhu, Amir Zeldes. 2023-06-03. GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation. https://arxiv.org/abs/2306.01966

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