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

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 6640–6650, November 16–20, 2020. c⃝2020 Association for Computational Linguistics 6640 Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations Lingzhi Wang1,2, Jing Li3, Xingshan Zeng1,2∗, Haisong Zhang4, Kam-Fai Wong1,2 1The Chinese University of Hong Kong, Hong Kong, China 2MoE Key Laboratory of High Confidence Software Technologies, China 3Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China 4Tencent AI Lab, China 1,2{lzwang,xszeng,kfwong}@se.cuhk.edu.hk 3jing-amelia.li@polyu.edu.hk, 4hansonzhang@tencent.com Abstract Quotations are crucial for successful explana- tions and persuasions in interpersonal commu- nications. 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 af- fects whether a quotation fits the given context. Here, we capture the contextual consistency of a quotation in terms of latent topics, interac- tions with the dialogue history, and coherence to the query turn’s existing content. Further, an encoder-decoder neural framework is em- ployed to continue the context with a quotation via language generation. Experiment results on two large-scale datasets in English and Chi- nese 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. 1 Introduction Quotations, or quotes, are memorable phrases or sentences widely echoed to spread patterns of wis- dom (Booten and Hearst, 2016). They are derived from the ancient art of rhetoric and now appear- ing in various daily activities, ranging from formal writings (Tan et al., 2015) to everyday conversa- tions (Lee et al., 2016), all help us present clear, beautiful, and persuasive language. However, for many individuals, writing a suitable quotation that fits the ongoing contexts is a daunting task. The issue becomes more pressing for quoting in online conversations where quick responses are usually needed on mobile devices (Lee et al., 2016). To help online users find what to quote in the discussions they are involved in, our work studies how to recommend an ongoing conversation with ∗Xingshan Zeng is the corresponding author. [T1]: Save your money. Scuf is the biggest ripoff in gaming. [T2]: What would you suggest instead? [T3]: Just use a normal controller. [T4]: Ooooooh, I get it now...you’re just dumb. [Q]: The dumb ones are the people spending over $100 for a controller. A fool and his money are soon parted. Figure 1: A Reddit conversation snippet about buying a Scuf controller. The quotation is in blue and italic. [T1] to [T4] are history turns while [Q] is for query turn. a quote and ensure its continuity of senses with the existing contexts. For task illustration, Figure 1 displays a Reddit conversation snippet centered around the worthiness to buy a Scuf controller. To argue against T4’s viewpoint, we see the query turn quotes Tusser’s old saying for showing that buying a controller is a waste of money. As can be observed, it is …

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