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This paper studies the task of best counter-argument retrieval given an inputargument. Following the definition that the best counter-argument addresses thesame aspects as the input argument while having the opposite stance, we aim todevelop an efficient and effective model for scoring counter-arguments based onsimilarity and dissimilarity metrics. We first conduct an experimental study onthe effectiveness of available scoring methods, including traditionalLearning-To-Rank (LTR) and recent neural scoring models. We then proposeBipolar-encoder, a novel BERT-based model to learn an optimal representationfor simultaneous similarity and dissimilarity. Experimental results show thatour proposed method can achieve the accuracy@1 of 49.04\%, which significantlyoutperforms other baselines by a large margin. When combined with anappropriate caching technique, Bipolar-encoder is comparably efficient atprediction time.

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