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    <titleInfo>
        <title>A Two-stage Generative Chinese AMR Parsing Method Based on Large Language Models</title>
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        <namePart type="given">Shen</namePart>
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            <title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)</title>
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            <namePart type="given">Hongfei</namePart>
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    <abstract>“The purpose of the CAMR task is to convert natural language into a formalized semantic representation in the form of a graph structure. Due to the complexity of the AMR graph structure, traditional AMR automatic parsing methods often require the design of complex models and strategies. Thanks to the powerful generative capabilities of LLMs, adopting an autore-gressive generative approach for AMR parsing has many advantages such as simple modeling and strong extensibility. To further explore the generative AMR automatic parsing technology based on LLMs, we design a two-stage AMR automatic parsing method based on LLMs in this CAMR evaluation. Specifically, we design two pipeline subtasks of alignment-aware node generation and relationship-aware node generation to reduce the difficulty of LLM understanding and generation. Additionally, to boost the system‘s transferability, we incorporate a retrieval-augmented strategy during both training and inference phases. The experimental results show that the method we proposed has achieved promising results in this evaluation.”</abstract>
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%0 Conference Proceedings
%T A Two-stage Generative Chinese AMR Parsing Method Based on Large Language Models
%A Zizhuo, Shen
%A Yanqiu, Shao
%A Wei, Li
%Y Lin, Hongfei
%Y Tan, Hongye
%Y Li, Bin
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F zizhuo-etal-2024-two
%X “The purpose of the CAMR task is to convert natural language into a formalized semantic representation in the form of a graph structure. Due to the complexity of the AMR graph structure, traditional AMR automatic parsing methods often require the design of complex models and strategies. Thanks to the powerful generative capabilities of LLMs, adopting an autore-gressive generative approach for AMR parsing has many advantages such as simple modeling and strong extensibility. To further explore the generative AMR automatic parsing technology based on LLMs, we design a two-stage AMR automatic parsing method based on LLMs in this CAMR evaluation. Specifically, we design two pipeline subtasks of alignment-aware node generation and relationship-aware node generation to reduce the difficulty of LLM understanding and generation. Additionally, to boost the system‘s transferability, we incorporate a retrieval-augmented strategy during both training and inference phases. The experimental results show that the method we proposed has achieved promising results in this evaluation.”
%U https://aclanthology.org/2024.ccl-3.17/
%P 154-159

Markdown (Informal)

[A Two-stage Generative Chinese AMR Parsing Method Based on Large Language Models](https://aclanthology.org/2024.ccl-3.17/) (Zizhuo et al., CCL 2024)

• A Two-stage Generative Chinese AMR Parsing Method Based on Large Language Models (Zizhuo et al., CCL 2024)

ACL

• Shen Zizhuo, Shao Yanqiu, and Li Wei. 2024. A Two-stage Generative Chinese AMR Parsing Method Based on Large Language Models. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 154–159, Taiyuan, China. Chinese Information Processing Society of China.