Research on identification of argument relation based on Scibert and Bilstm-CRF

Pengfei Shi; Caiquan Xiong; Xinyun Wu

doi:10.1117/12.2639865

19 October 2022 Research on identification of argument relation based on Scibert and Bilstm-CRF

Pengfei Shi, Caiquan Xiong, Xinyun Wu

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122943A (2022) https://doi.org/10.1117/12.2639865
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

The identification of argument relation is an important subtask of argumentation mining. Its purpose is to identify the support or attack relationship between two argument components, so that people can understand the argument process in the argumentative text more deeply. This paper proposes a research method based on Scibert and BILSTM-CRF model. First, the pre-trained language model Scibert dynamically obtains word vectors, and then combines with the BiLSTM network to fine-tune downstream tasks to obtain contextual information. Finally, the argument relation is identified by conditional random field. Experiments were conducted on two argumentation mining datasets, Persuasive Essays and UKP Sentential Corpus, which were published in Germany. The experimental results show that our method is better than the baseline method.

Citation Download Citation

Pengfei Shi, Caiquan Xiong, and Xinyun Wu "Research on identification of argument relation based on Scibert and Bilstm-CRF", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122943A (19 October 2022); https://doi.org/10.1117/12.2639865

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KEYWORDS

Mining

Computer programming

Neural networks

Transformers

Machine learning

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