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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