论文标题
通过关键字引导网络产生信息性的对话响应
Generating Informative Dialogue Responses with Keywords-Guided Networks
论文作者
论文摘要
最近，开放域对话系统引起了人们的关注。他们中的大多数使用序列到序列（SEQ2SEQ）体系结构来生成响应。但是，传统的基于SEQ2SEQ的开放域对话模型倾向于产生通用和安全的响应，与人类的反应不同，信息不足。在本文中，我们提出了一个简单但有效的关键字引导的顺序到序列模型（KW-SEQ2SEQ），该模型使用关键字信息作为指导来生成开放域对话响应。具体而言，KW-Seq2Seq首先使用关键字解码器来预测某些主题关键字，然后在其指导下生成最终响应。广泛的实验表明，KW-SEQ2SEQ模型产生更有信息，连贯和流利的响应，从而在自动和人类评估指标中产生实质性增长。
Recently, open-domain dialogue systems have attracted growing attention. Most of them use the sequence-to-sequence (Seq2Seq) architecture to generate responses. However, traditional Seq2Seq-based open-domain dialogue models tend to generate generic and safe responses, which are less informative, unlike human responses. In this paper, we propose a simple but effective keywords-guided Sequence-to-Sequence model (KW-Seq2Seq) which uses keywords information as guidance to generate open-domain dialogue responses. Specifically, KW-Seq2Seq first uses a keywords decoder to predict some topic keywords, and then generates the final response under the guidance of them. Extensive experiments demonstrate that the KW-Seq2Seq model produces more informative, coherent and fluent responses, yielding substantive gain in both automatic and human evaluation metrics.
