Abstract
Argument pair extraction (APE) is an important research area for real-world applications such as online debates and persuasive essays. To facilitate the research of APE, NLPCC 2021 shared Task1 releases the RR-dataset, which aims to extract argument pairs from the peer reviews and its rebuttals. We propose a two-stage learning strategy for this task which divides it into two sub-tasks: arguments mining and arguments pairing. In arguments pairing task, instead of modeling arguments pairing task in sentence level, we cast it as a paragraph-level pairing task, which can alleviate the mismatch between the definition of task and training. And we apply transfer learning and fine-tuning strategy on all sub-tasks, which exploits large scale pre-trained semantic knowledge to benefit downstream APE task. Experiment results show that our method achieves significant improvements compared with strong baseline BERT-based multi-task learning framework, and finally ranks the \(3^{rd}\) in NLPCC2021 shared task 1 track 3 evaluation phrase.
S. Wang—Author contributed equally.
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We thank the reviewers for their careful reviewing and valuable advises, which are important for us to improve our work.
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Wang, S., Yin, Z., Zhang, W., Zheng, D., Li, X. (2021). Two Stage Learning for Argument Pairs Extraction. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13029. Springer, Cham. https://doi.org/10.1007/978-3-030-88483-3_44
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