论文标题
基于事实的对话产生，分解和分解分解
Fact-based Dialogue Generation with Convergent and Divergent Decoding
论文作者
论文摘要
基于事实的对话生成是基于对话上下文和事实文本产生类似人类的响应的任务。提出了各种方法，专注于产生有效包含事实的信息词。但是，以前的作品隐含地假设一个主题要在对话中保存，并且通常是被动的，因此系统很难产生多种响应，以主动提供有意义的信息。本文提出了一个基于端到端的事实对话系统，并具有在上下文和事实上的收敛性和分歧思维能力，这可以就当前主题进行交谈或引入新主题。具体而言，我们的模型结合了一种新颖的收敛性和不同的解码，该解码不仅可以考虑给定输入（上下文和事实），还可以产生内容丰富和多样化的响应。 DSTC7数据集上的自动评估结果都表明，我们的模型的表现明显胜过最先进的基线，这表明我们的模型可以产生更合适，信息性和多样化的响应。
Fact-based dialogue generation is a task of generating a human-like response based on both dialogue context and factual texts. Various methods were proposed to focus on generating informative words that contain facts effectively. However, previous works implicitly assume a topic to be kept on a dialogue and usually converse passively, therefore the systems have a difficulty to generate diverse responses that provide meaningful information proactively. This paper proposes an end-to-end fact-based dialogue system augmented with the ability of convergent and divergent thinking over both context and facts, which can converse about the current topic or introduce a new topic. Specifically, our model incorporates a novel convergent and divergent decoding that can generate informative and diverse responses considering not only given inputs (context and facts) but also inputs-related topics. Both automatic and human evaluation results on DSTC7 dataset show that our model significantly outperforms state-of-the-art baselines, indicating that our model can generate more appropriate, informative, and diverse responses.
