  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.
