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        <title>Negation Scope Resolution for Chinese as a Second Language</title>
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            <title>Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Negation Scope Resolution for Chinese as a Second Language
%A Zhang, Mengyu
%A Wang, Weiqi
%A Sun, Shuqiao
%A Sun, Weiwei
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Kochmar, Ekaterina
%Y Laarmann-Quante, Ronja
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Yannakoudakis, Helen
%Y Zesch, Torsten
%S Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2021-negation
%X 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.
%U https://aclanthology.org/2021.bea-1.1
%P 1-10

Markdown (Informal)

[Negation Scope Resolution for Chinese as a Second Language](https://aclanthology.org/2021.bea-1.1) (Zhang et al., BEA 2021)

• Negation Scope Resolution for Chinese as a Second Language (Zhang et al., BEA 2021)

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.