基于检索的聊天机器人多回合响应选择的多表示融合网络

Chongyang Tao, Wei Wu, Can Xu, Wenpeng Hu, Dongyan Zhao, Rui Yan
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引用次数: 126

摘要

在基于检索的聊天机器人中,我们考虑了上下文-响应匹配与多种类型表示的多回合响应选择。表征对单词、n-gram和话语子序列上的上下文语义和反应进行编码,并捕获单词之间的短期和长期依赖关系。有了这么多的表征,我们研究如何将它们融合到一个深度神经结构中进行匹配,以及每个表征如何对匹配做出贡献。为此,我们提出了一种多表示融合网络,其中表示可以在早期阶段,中间阶段或最后阶段融合到匹配中。我们在两个基准数据集上比较了不同的表示和融合策略。评估结果表明,后期融合总是优于早期融合,并且通过在最后阶段融合表征,我们的模型显著优于现有方法,并在两个数据集上实现了新的最先进的性能。通过深入的烧蚀研究,我们证明了每种表示对匹配的影响,这有助于在实际系统中如何选择它们。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Representation Fusion Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
We consider context-response matching with multiple types of representations for multi-turn response selection in retrieval-based chatbots. The representations encode semantics of contexts and responses on words, n-grams, and sub-sequences of utterances, and capture both short-term and long-term dependencies among words. With such a number of representations in hand, we study how to fuse them in a deep neural architecture for matching and how each of them contributes to matching. To this end, we propose a multi-representation fusion network where the representations can be fused into matching at an early stage, at an intermediate stage, or at the last stage. We empirically compare different representations and fusing strategies on two benchmark data sets. Evaluation results indicate that late fusion is always better than early fusion, and by fusing the representations at the last stage, our model significantly outperforms the existing methods, and achieves new state-of-the-art performance on both data sets. Through a thorough ablation study, we demonstrate the effect of each representation to matching, which sheds light on how to select them in practical systems.
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