Document Grounded Conversations is a task to generate dialogue responses when
chatting about the content of a given document. Obviously, document knowledge
plays a critical role in Document Grounded Conversations, while existing
dialogue models do not exploit this kind of knowledge effectively enough. In
this paper, we propose a novel Transformer-based architecture for multi-turn
document grounded conversations. In particular, we devise an Incremental
Transformer to encode multi-turn utterances along with knowledge in related
documents. Motivated by the human cognitive process, we design a two-pass
decoder (Deliberation Decoder) to improve context coherence and knowledge
correctness. Our empirical study on a real-world Document Grounded Dataset
proves that responses generated by our model significantly outperform
competitive baselines on both context coherence and knowledge relevance.

本文提出了一种基于 Transformer 的架构和增量式 Transformer 编码多轮言语和相关文档知识以及两阶段解码器（Deliberation Decoder）来提高上下文连贯性和知识正确性的新方法，实证研究表明，我们的模型生成的响应在上下文连贯性和知识相关性方面显著优于竞争基线。

基于 Deliberation 解码器的增量式 Transformer 模型用于文档引导会话

Incremental Transformer with Deliberation Decoder for Document Grounded  Conversations

This paper introduces a document grounded dataset for text conversations. We
define "Document Grounded Conversations" as conversations that are about the
contents of a specified document. In this dataset the specified documents were
Wikipedia articles about popular movies. The dataset contains 4112
conversations with an average of 21.43 turns per conversation. This positions
this dataset to not only provide a relevant chat history while generating
responses but also provide a source of information that the models could use.
We describe two neural architectures that provide benchmark performance on the
task of generating the next response. We also evaluate our models for
engagement and fluency, and find that the information from the document helps
in generating more engaging and fluent responses.

本研究介绍了一个文档驱动的文本对话数据集，包含关于热门电影的维基百科文章，利用两种神经网络模型对话，结果表明这种方法引入了更多信息，提高了生成回答的流畅度和吸引力。