OCNLI: Original Chinese Natural Language Inference

Hai Hu, Kyle Richardson, Liang Xu, Lu Li, Sandra Kübler, Lawrence Moss


Abstract
Despite the tremendous recent progress on natural language inference (NLI), driven largely by large-scale investment in new datasets (e.g.,SNLI, MNLI) and advances in modeling, most progress has been limited to English due to a lack of reliable datasets for most of the world’s languages. In this paper, we present the first large-scale NLI dataset (consisting of ~56,000 annotated sentence pairs) for Chinese called the Original Chinese Natural Language Inference dataset (OCNLI). Unlike recent attempts at extending NLI to other languages, our dataset does not rely on any automatic translation or non-expert annotation. Instead, we elicit annotations from native speakers specializing in linguistics. We follow closely the annotation protocol used for MNLI, but create new strategies for eliciting diverse hypotheses. We establish several baseline results on our dataset using state-of-the-art pre-trained models for Chinese, and find even the best performing models to be far outpaced by human performance (~12% absolute performance gap), making it a challenging new resource that we hope will help to accelerate progress in Chinese NLU. To the best of our knowledge, this is the first human-elicited MNLI-style corpus for a non-English language.
Anthology ID:
2020.findings-emnlp.314
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3512–3526
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.314
DOI:
10.18653/v1/2020.findings-emnlp.314
Bibkey:
Cite (ACL):
Hai Hu, Kyle Richardson, Liang Xu, Lu Li, Sandra Kübler, and Lawrence Moss. 2020. OCNLI: Original Chinese Natural Language Inference. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3512–3526, Online. Association for Computational Linguistics.
Cite (Informal):
OCNLI: Original Chinese Natural Language Inference (Hu et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.314.pdf
Code
 CLUEbenchmark/OCNLI
Data
OCNLICLUEGLUESNLISuperGLUEXNLI