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 88.9\%, which significantly
outperforms other baselines by a large margin. When combined with an
appropriate caching technique, Bipolar-encoder is comparably efficient at
prediction time.

本文提出了一种基于 Bert 的新模型 Bipolar-encoder，通过相似度和不相似度度量来评分反驳论证，并通过实验证明了其比传统方法和最近的神经评分模型更加有效和高效。