Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations

Lingzhi Wang, Jing Li, Xingshan Zeng, Haisong Zhang, Kam-fai Wong

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


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 contents. 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.
Original languageEnglish
Title of host publication2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Number of pages11
Publication statusPublished - Nov 2020


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