Revisiting the Role of Similarity and Dissimilarity in Best Counter Argument Retrieval

• URL: http://arxiv.org/abs/2304.08807v2
• Date: Wed, 19 Apr 2023 03:04:28 GMT
• Title: Revisiting the Role of Similarity and Dissimilarity in Best Counter Argument Retrieval
• Authors: Hongguang Shi, Shuirong Cao, Cam-Tu Nguyen
• Abstract summary: We develop an efficient model for scoring counter-arguments based on similarity and dissimilarity metrics. We propose Bipolar-encoder, a novel BERT-based model to learn an optimal representation for simultaneous similarity and dissimilarity. Experimental results show that our proposed method can achieve the accuracy@1 of 49.04%, which significantly outperforms other baselines by a large margin.
• Score: 1.7607244667735586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper studies the task of best counter-argument retrieval given an input argument. Following the definition that the best counter-argument addresses the same aspects as the input argument while having the opposite stance, we aim to develop an efficient and effective model for scoring counter-arguments based on similarity and dissimilarity metrics. We first conduct an experimental study on the effectiveness of available scoring methods, including traditional Learning-To-Rank (LTR) and recent neural scoring models. We then propose Bipolar-encoder, a novel BERT-based model to learn an optimal representation for simultaneous similarity and dissimilarity. Experimental results show that our proposed method can achieve the accuracy@1 of 49.04\%, which significantly outperforms other baselines by a large margin. When combined with an appropriate caching technique, Bipolar-encoder is comparably efficient at prediction time.

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