Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
Lianhui Qin†, Michel Galley‡, Chris Brockett‡, Xiaodong Liu‡, Xiang Gao‡, Bill Dolan‡, Yejin Choi† and Jianfeng Gao‡ † University of Washington, Seattle, WA, USA ‡ Microsoft Research, Redmond, WA, USA
Abstract
Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous. We present a new end-to-end approach to contentful neural conversation that jointly models response generation and on-demand machine reading. The key idea is to provide the conversation model with relevant long-form text on the fly as a source of external knowledge. The model performs QA-style reading comprehension on this text in response to each conversational turn, thereby allowing for more focused integration of external knowledge than has been possible in prior approaches. To support further research on knowledge-grounded conversation, we introduce a new large-scale conversation dataset grounded in external web pages (2.8M turns, 7.4M sentences of grounding). Both human evaluation and automated metrics show that our approach results in more contentful responses compared to a variety of previous methods, improving both the informativeness and diversity of generated output.
中文速览
神经对话模型虽然能生成流畅的回复，却常常内容空洞、缺乏实质信息，根本原因在于模型不知道"该说什么"。研究者提出了一个名为CMR（Conversation with on-demand Machine Reading，按需机器阅读对话）的端到端框架，将对话历史类比为问题、待生成回复类比为答案，把机器阅读理解（MRC）机制嵌入对话生成过程中，让模型在每一轮对话时动态读取并理解相关的长篇网页文档，从而将外部知识更精准地融入回复生成。为支撑这一研究方向，作者还从Reddit中构建了一个大规模知识驱动对话数据集，包含280万轮对话和740万句背景文本，比现有同类数据集大一个数量级以上。人工评测和自动指标均表明，CMR生成的回复在信息量和多样性上都显著优于多种基线方法，证明了"深度外部知识融合"对于构建有实质内容的对话系统的重要价值。
原文 arXiv:1906.02738;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1906.02738v2