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.

我们提出了一种新的端到端方法，用于内容丰富的神经会话，它将响应生成和按需的机器阅读共同建模，通过在飞行时向对话模型提供相关的长形文本作为外部知识源，每次进行问答式阅读来响应每个对话轮换，支持进一步的知识基础会话研究，我们介绍了一个新的基于外部网页的大规模会话数据集（2.8M 轮，7.4M 语句），人类评估和自动指标表明，我们的方法相对于各种先前方法，可以获得更加富有内容的响应，从而提高所生成的输出的信息量和多样性。