Quotations are crucial for successful explanations and persuasions in
interpersonal communications. However, finding what to quote in a conversation
is challenging for both humans and machines. This work studies automatic
quotation generation in an online conversation and explores how language
consistency affects whether a quotation fits the given context. Here, we
capture the contextual consistency of a quotation in terms of latent topics,
interactions with the dialogue history, and coherence to the query turn's
existing content. Further, an encoder-decoder neural framework is employed to
continue the context with a quotation via language generation. Experiment
results on two large-scale datasets in English and Chinese demonstrate that our
quotation generation model outperforms the state-of-the-art models. Further
analysis shows that topic, interaction, and query consistency are all helpful
to learn how to quote in online conversations.

本研究探讨了在线对话中自动引用生成的一些特点，基于对话历史、语言一致性的相互作用，以及与查询转的现有内容的一致性等方面对引文的语境一致性进行了捕捉，并采用编码 - 解码神经框架通过语言生成来继续语境，实现引文的自动化。通过英文和中文两个大规模数据集进行实验，结果表明我们的引文生成模型优于现有的最先进模型。进一步的分析显示，主题、交互和查询的一致性都有助于学习如何在在线对话中引用。