Cross-domain Chinese Sentence Pattern Parsing
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914743250321408 |
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| author | Yu, Jingsi Kong, Cunliang Yang, Liner Zhang, Meishan Zhu, Lin Wang, Yujie Lin, Haozhe Sun, Maosong Yang, Erhong |
| author_facet | Yu, Jingsi Kong, Cunliang Yang, Liner Zhang, Meishan Zhu, Lin Wang, Yujie Lin, Haozhe Sun, Maosong Yang, Erhong |
| contents | Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching.Existing SPS parsers rely heavily on textbook corpora for training, lacking cross-domain capability.To overcome this constraint, this paper proposes an innovative approach leveraging large language models (LLMs) within a self-training framework. Partial syntactic rules from a source domain are combined with target domain sentences to dynamically generate training data, enhancing the adaptability of the parser to diverse domains.Experiments conducted on textbook and news domains demonstrate the effectiveness of the proposed method, outperforming rule-based baselines by 1.68 points on F1 metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16311 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cross-domain Chinese Sentence Pattern Parsing Yu, Jingsi Kong, Cunliang Yang, Liner Zhang, Meishan Zhu, Lin Wang, Yujie Lin, Haozhe Sun, Maosong Yang, Erhong Computation and Language Artificial Intelligence Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching.Existing SPS parsers rely heavily on textbook corpora for training, lacking cross-domain capability.To overcome this constraint, this paper proposes an innovative approach leveraging large language models (LLMs) within a self-training framework. Partial syntactic rules from a source domain are combined with target domain sentences to dynamically generate training data, enhancing the adaptability of the parser to diverse domains.Experiments conducted on textbook and news domains demonstrate the effectiveness of the proposed method, outperforming rule-based baselines by 1.68 points on F1 metrics. |
| title | Cross-domain Chinese Sentence Pattern Parsing |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2402.16311 |