Abstract
This paper studies Negation Scope Resolution (NSR) for Chinese as a Second Language (CSL), which shows many unique characteristics that distinguish itself from “standard” Chinese. We annotate a new moderate-sized corpus that covers two background L1 languages, viz. English and Japanese. We build a neural NSR system, which achieves a new state-of-the-art accuracy on English benchmark data. We leverage this system to gauge how successful NSR for CSL can be. Different native language backgrounds of language learners result in unequal cross-lingual transfer, which has a significant impact on processing second language data. In particular, manual annotation, empirical evaluation and error analysis indicate two non-obvious facts: 1) L2-Chinese, L1-Japanese data are more difficult to analyze and thus annotate than L2-Chinese, L1-English data; 2) computational models trained on L2-Chinese, L1-Japanese data perform better than models trained on L2-Chinese, L1-English data.- Anthology ID:
- 2021.bea-1.1
- Volume:
- Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–10
- Language:
- URL:
- https://aclanthology.org/2021.bea-1.1
- DOI:
- Cite (ACL):
- Mengyu Zhang, Weiqi Wang, Shuqiao Sun, and Weiwei Sun. 2021. Negation Scope Resolution for Chinese as a Second Language. In Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, pages 1–10, Online. Association for Computational Linguistics.
- Cite (Informal):
- Negation Scope Resolution for Chinese as a Second Language (Zhang et al., BEA 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.bea-1.1.pdf