Resolving Chinese Anaphora with ChatGPT

This study evaluates ChatGPT's ability to resolve Chinese anaphora using a newly developed Chinese collection of Winograd schemas, achieving an impressive accuracy rate of 91.5%. The results illustrate ChatGPT's effectiveness across multiple languages, placing it favorably alongside other prominent language models and showcasing its capabilities in addressing complex anaphora resolution challenges. Employing a refined methodological approach, the research enhanced schema collections to better capture Chinese linguistic idiosyncrasies and integrated detailed reasoning analyses for model evaluations. The findings highlight the considerable promise of transformer-based models like ChatGPT in advancing Natural Language Processing (NLP) applications beyond English, contributing to global linguistic inclusivity. Furthermore, the study emphasizes the necessity for ongoing research aimed at expanding language model coverage to include other languages and incorporating more extensive contextual and commonsense knowledge into NLP technologies.

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Publication Details

Published
2024-08-04
DOI
https://doi.org/10.1109/ialp63756.2024.10661112
Citations
1
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
0.35
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article

Resolving Chinese Anaphora with ChatGPT

Shuangshuang Chen
1 citations
Topic Modeling
0.35
article

Resolving Chinese Anaphora with ChatGPT

Shuangshuang Chen
article en
1 citations

Abstract

This study evaluates ChatGPT's ability to resolve Chinese anaphora using a newly developed Chinese collection of Winograd schemas, achieving an impressive accuracy rate of 91.5%. The results illustrate ChatGPT's effectiveness across multiple languages, placing it favorably alongside other prominent language models and showcasing its capabilities in addressing complex anaphora resolution challenges. Employing a refined methodological approach, the research enhanced schema collections to better capture Chinese linguistic idiosyncrasies and integrated detailed reasoning analyses for model evaluations. The findings highlight the considerable promise of transformer-based models like ChatGPT in advancing Natural Language Processing (NLP) applications beyond English, contributing to global linguistic inclusivity. Furthermore, the study emphasizes the necessity for ongoing research aimed at expanding language model coverage to include other languages and incorporating more extensive contextual and commonsense knowledge into NLP technologies.

Fudan University (CN)
Zero hunger
Openalex Percentile: Top 34%
Topic Modeling
0.35
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Resolving Chinese Anaphora with ChatGPT — Shuangshuang Chen · (2024) | TGRS Research Map | TGRS