论文标题
目标引导的情感意识聊天机
Target Guided Emotion Aware Chat Machine
论文作者
论文摘要
在语义层面和情感层面上对给定帖子的响应的一致性对于提供类似人类互动的对话系统至关重要。但是，在文献中，这一挑战并没有很好地解决，因为大多数方法在产生响应时忽略了帖子传达的情感信息。本文通过提出一个独立的端到端神经体系结构来解决此问题，该结构能够同时编码帖子中的语义和情感，并利用目标信息，以通过适当表达的情绪产生更聪明的响应。关于现实世界数据的广泛实验表明，所提出的方法在内容连贯性和情感适当性方面优于最先进的方法。
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem by proposing a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post and leverage target information for generating more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.
