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.

翻译：本文研究了给定输入论点时最佳反驳论点检索的任务。根据最佳反驳论点应与输入论点关注相同方面但持相反立场的定义，我们致力于开发一种基于相似性和差异性度量对反驳论点进行评分的高效且有效的模型。我们首先对现有评分方法的有效性进行了实验研究，包括传统的学习排序（LTR）和最近的神经评分模型。随后，我们提出了Bipolar-encoder，一种基于BERT的新型模型，用于学习同时优化相似性和差异性的表示。实验结果表明，我们提出的方法在准确率@1上达到49.04%，显著优于其他基线方法。当与适当的缓存技术结合时，Bipolar-encoder在预测阶段具有相当高的效率。