Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations
This work addresses the challenge of finding relevant quotations for explanations and persuasions in interpersonal communications, which is incremental as it builds on existing neural frameworks.
The paper tackles the problem of automatic quotation generation in online conversations by exploring how language consistency affects quotation fit, and demonstrates that their model outperforms state-of-the-art models on English and Chinese datasets.
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.