Abstract
Intelligent personal assistant systems, with either text-based or voice-based conversational interfaces, are becoming increasingly popular. Most previous research has used either retrieval-based or generation-based methods. Retrieval-based methods have the advantage of returning fluent and informative responses with great diversity. The retrieved responses are easier to control and explain. However, the response retrieval performance is limited by the size of the response repository. On the other hand, although generation-based methods can return highly coherent responses given conversation context, they are likely to return universal or general responses with insufficient ground knowledge information. In this paper, we build a hybrid neural conversation model with the capability of both response retrieval and generation, in order to combine the merits of these two types of methods. Experimental results on Twitter and Foursquare data show that the proposed model can outperform both retrieval-based methods and generation-based methods (including a recently proposed knowledge-grounded neural conversation model) under both automatic evaluation metrics and human evaluation. Our models and research findings provide new insights on how to integrate text retrieval and text generation models for building conversation systems.
Abstract (translated)
智能个人助理系统，无论是基于文本还是基于语音的会话界面，都越来越流行。大多数以前的研究都使用了基于检索或基于生成的方法。基于检索的方法具有返回流畅、信息量大、多样性强的优点。检索到的响应更容易控制和解释。但是，响应检索性能受响应存储库大小的限制。另一方面，尽管基于代的方法可以在给定的会话上下文中返回高度一致的响应，但它们可能返回具有不充分基础知识信息的通用或通用响应。本文将这两种方法的优点结合起来，建立了具有响应检索和生成能力的混合神经会话模型。在twitter和foursquare上的实验结果表明，在自动评估指标和人工评估的情况下，该模型优于基于检索的方法和基于生成的方法（包括最近提出的基于知识的神经会话模型）。我们的模型和研究结果为如何集成文本检索和文本生成模型来构建对话系统提供了新的见解。
URL
https://arxiv.org/abs/1904.09068
