Abstract
This paper describes our participating system run to the argumentative text understanding shared task for AI Debater at NLPCC 2021 (http://www.fudan-disc.com/sharedtask/AIDebater21/tracks.html). The tasks are motivated towards developing an autonomous debating system. We make an initial attempt with Track-3, namely, argument pair extraction from peer review and rebuttal where we extract arguments from peer reviews and their corresponding rebuttals from author responses. Compared to the multi-task baseline by the organizers, we introduce two significant changes: (i) we use ERNIE 2.0 token embedding, which can better capture lexical, syntactic, and semantic aspects of information in the training data, (ii) we perform double attention learning to capture long-term dependencies. Our proposed model achieves the state-of-the-art results with a relative improvement of 8.81% in terms of F1 score over the baseline model. We make our code available publicly at https://github.com/guneetsk99/ArgumentMining_SharedTask. Our team ARGUABLY is one of the third prize-winning teams in Track 3 of the shared task.
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Kohli, G.S., Kaur, P., Singh, M., Ghosal, T., Rana, P.S. (2021). ARGUABLY @ AI Debater-NLPCC 2021 Task 3: Argument Pair Extraction from Peer Review and Rebuttals. 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_48
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