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

• URL: http://arxiv.org/abs/2106.09896v1
• Date: Fri, 18 Jun 2021 03:38:48 GMT
• Title: Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations
• Authors: Lingzhi Wang, Jing Li, Xingshan Zeng, Haisong Zhang, Kam-Fai Wong
• Abstract summary: This work studies automatic quotation generation in an online conversation. An encoder-decoder neural framework is employed to continue the context with a quotation. Experiment results on two large-scale datasets in English and Chinese.
• Score: 23.214585012203084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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