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        <title>LLM-enhanced Self-training for Cross-domain Constituency Parsing</title>
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        <namePart type="given">Jianling</namePart>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Self-training has proven to be an effective approach for cross-domain tasks, and in this study, we explore its application to cross-domain constituency parsing. Traditional self-training methods rely on limited and potentially low-quality raw corpora. To overcome this limitation, we propose enhancing self-training with the large language model (LLM) to generate domain-specific raw corpora iteratively. For the constituency parsing, we introduce grammar rules that guide the LLM in generating raw corpora and establish criteria for selecting pseudo instances. Our experimental results demonstrate that self-training for constituency parsing, equipped with an LLM, outperforms traditional methods regardless of the LLM’s performance. Moreover, the combination of grammar rules and confidence criteria for pseudo-data selection yields the highest performance in the cross-domain constituency parsing.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.508</identifier>
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%0 Conference Proceedings
%T LLM-enhanced Self-training for Cross-domain Constituency Parsing
%A Li, Jianling
%A Zhang, Meishan
%A Guo, Peiming
%A Zhang, Min
%A Zhang, Yue
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-llm
%X Self-training has proven to be an effective approach for cross-domain tasks, and in this study, we explore its application to cross-domain constituency parsing. Traditional self-training methods rely on limited and potentially low-quality raw corpora. To overcome this limitation, we propose enhancing self-training with the large language model (LLM) to generate domain-specific raw corpora iteratively. For the constituency parsing, we introduce grammar rules that guide the LLM in generating raw corpora and establish criteria for selecting pseudo instances. Our experimental results demonstrate that self-training for constituency parsing, equipped with an LLM, outperforms traditional methods regardless of the LLM’s performance. Moreover, the combination of grammar rules and confidence criteria for pseudo-data selection yields the highest performance in the cross-domain constituency parsing.
%R 10.18653/v1/2023.emnlp-main.508
%U https://aclanthology.org/2023.emnlp-main.508
%U https://doi.org/10.18653/v1/2023.emnlp-main.508
%P 8174-8185

Markdown (Informal)

[LLM-enhanced Self-training for Cross-domain Constituency Parsing](https://aclanthology.org/2023.emnlp-main.508) (Li et al., EMNLP 2023)

• LLM-enhanced Self-training for Cross-domain Constituency Parsing (Li et al., EMNLP 2023)

ACL

• Jianling Li, Meishan Zhang, Peiming Guo, Min Zhang, and Yue Zhang. 2023. LLM-enhanced Self-training for Cross-domain Constituency Parsing. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 8174–8185, Singapore. Association for Computational Linguistics.