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.

翻译：本文研究给定输入论点下的最优反驳论点检索任务。根据定义，最优反驳论点需与输入论点涉及相同方面但持有相反立场，我们旨在开发一种基于相似性和相异性度量来高效有效地对反驳论点进行评分的模型。我们首先对现有评分方法的有效性进行实验研究，包括传统的学习排序（LTR）方法和最新的神经评分模型。随后，我们提出双极编码器（Bipolar-encoder），一种基于BERT的新型模型，用于学习同时表征相似性和相异性的最优表示。实验结果表明，我们提出的方法能够达到88.9%的精度@1，显著优于其他基线模型。结合适当的缓存技术后，双极编码器在预测阶段也具有相当的效率。