TY - GEN
T1 - Modeling Inter Round Attack of Online Debaters for Winner Prediction
AU - Hsiao, Fa Hsuan
AU - Yen, An Zi
AU - Huang, Hen Hsen
AU - Chen, Hsin Hsi
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/4/25
Y1 - 2022/4/25
N2 - In a debate, two debaters with opposite stances put forward arguments to fight for their viewpoints. Debaters organize their arguments to support their proposition and attack opponents' points. The common purpose of debating is to persuade the opponents and the audiences to agree with the mentioned propositions. Previous works have investigated the issue of identifying which debater is more persuasive. However, modeling the interaction of arguments between rounds is rarely discussed. In this paper, we focus on assessing the overall performance of debaters in a multi-round debate on online forums. To predict the winner in a multi-round debate, we propose a novel neural model that is aimed at capturing the interaction of arguments by exploiting raw text, structure information, argumentative discourse units (ADUs), and the relations among ADUs. Experimental results show that our model achieves competitive performance compared with the existing models, and is capable of extracting essential argument relations during a multi-round debate by leveraging argumentative structure and attention mechanism.
AB - In a debate, two debaters with opposite stances put forward arguments to fight for their viewpoints. Debaters organize their arguments to support their proposition and attack opponents' points. The common purpose of debating is to persuade the opponents and the audiences to agree with the mentioned propositions. Previous works have investigated the issue of identifying which debater is more persuasive. However, modeling the interaction of arguments between rounds is rarely discussed. In this paper, we focus on assessing the overall performance of debaters in a multi-round debate on online forums. To predict the winner in a multi-round debate, we propose a novel neural model that is aimed at capturing the interaction of arguments by exploiting raw text, structure information, argumentative discourse units (ADUs), and the relations among ADUs. Experimental results show that our model achieves competitive performance compared with the existing models, and is capable of extracting essential argument relations during a multi-round debate by leveraging argumentative structure and attention mechanism.
KW - Argument Mining
KW - Debate Winner Prediction
KW - Inter Round Attack Attention
UR - http://www.scopus.com/inward/record.url?scp=85129891221&partnerID=8YFLogxK
U2 - 10.1145/3485447.3512006
DO - 10.1145/3485447.3512006
M3 - Conference contribution
AN - SCOPUS:85129891221
T3 - WWW 2022 - Proceedings of the ACM Web Conference 2022
SP - 2860
EP - 2869
BT - WWW 2022 - Proceedings of the ACM Web Conference 2022
PB - Association for Computing Machinery, Inc
T2 - 31st ACM World Wide Web Conference, WWW 2022
Y2 - 25 April 2022 through 29 April 2022
ER -