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:本文研究了在给定输入论点的情况下进行最佳对抗论点检索的任务。遵循最佳对抗论点的定义,该论点与输入论点相反,但涉及相同的方面。我们旨在开发一种高效有效的模型,基于相似性和不相似性度量对反驳论点进行评分。我们首先对现有评分方法进行了实验研究,包括传统的学习排序(LTR)和最近的神经评分模型。然后,我们提出了Bipolar-encoder,一种基于BERT的新型模型,用于学习同时相似性和不相似性的最佳表示。实验结果表明,我们提出的方法可以实现88.9%的准确率@1,且与其他基线相比有很大的优势。当与适当的缓存技术相结合时,Bipolar-encoder在预测时间上也是高效的。