Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915307297177600 |
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| author | Guo, Peiming Zhang, Meishan Li, Jianling Zhang, Min Zhang, Yue |
| author_facet | Guo, Peiming Zhang, Meishan Li, Jianling Zhang, Min Zhang, Yue |
| contents | Cross-domain constituency parsing is still an unsolved challenge in computational linguistics since the available multi-domain constituency treebank is limited. We investigate automatic treebank generation by large language models (LLMs) in this paper. The performance of LLMs on constituency parsing is poor, therefore we propose a novel treebank generation method, LLM back generation, which is similar to the reverse process of constituency parsing. LLM back generation takes the incomplete cross-domain constituency tree with only domain keyword leaf nodes as input and fills the missing words to generate the cross-domain constituency treebank. Besides, we also introduce a span-level contrastive learning pre-training strategy to make full use of the LLM back generation treebank for cross-domain constituency parsing. We verify the effectiveness of our LLM back generation treebank coupled with contrastive learning pre-training on five target domains of MCTB. Experimental results show that our approach achieves state-of-the-art performance on average results compared with various baselines. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_20976 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing Guo, Peiming Zhang, Meishan Li, Jianling Zhang, Min Zhang, Yue Computation and Language Cross-domain constituency parsing is still an unsolved challenge in computational linguistics since the available multi-domain constituency treebank is limited. We investigate automatic treebank generation by large language models (LLMs) in this paper. The performance of LLMs on constituency parsing is poor, therefore we propose a novel treebank generation method, LLM back generation, which is similar to the reverse process of constituency parsing. LLM back generation takes the incomplete cross-domain constituency tree with only domain keyword leaf nodes as input and fills the missing words to generate the cross-domain constituency treebank. Besides, we also introduce a span-level contrastive learning pre-training strategy to make full use of the LLM back generation treebank for cross-domain constituency parsing. We verify the effectiveness of our LLM back generation treebank coupled with contrastive learning pre-training on five target domains of MCTB. Experimental results show that our approach achieves state-of-the-art performance on average results compared with various baselines. |
| title | Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.20976 |