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

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.

Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations

“…Datasets. We conduct experiments based on datasets from two different platforms, Weibo and Reddit, released by Wang et al (2020). To make our experimental results comparable to Wang et al (2020), we utilize their preprocessed data directly.…”

“…4) CTIQ. The SOTA model (Wang et al, 2020), which employs an encoder-decoder framework enhanced by Neural Topic Model to continue the context with a quotation via language generation. 5) BERT.…”

ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding

“…Figure 1 shows two examples of narratives for a proverb from our dataset, along with corresponding alignment annotations. We diverge from related extant resources (Wang et al, 2020;Tan et al, 2015Tan et al, , 2016 on using proverbs in terms of quality of narratives, direct supervision and having fine-grained alignment annotations. 2 We explore three tasks: (1) proverb retrieval ( § 5.1) and alignment prediction, (2) narrative generation for a given proverb and a set of keywords specifying a topic ( § 5.2), and (3) discovering narratives with similar motifs ( § 5.3).…”

“…Prior work has explored recommending Chinese idioms as context-based recommendation (Liu et al, 2019b) or as cloze-style reading comprehension tasks (Zheng et al, 2019). Learning to quote has been explored based on fiction Tan et al (2015Tan et al ( , 2016 and noisy social media conversations from Twitter, Reddit or Weibo (Lee et al, 2016;Wang et al, 2020). In the most related prior work, authors explore a quote retrieval task borrowing inspiration from context based recommendation systems (Huang et al, 2012;He et al, 2010).…”

“…In the most related prior work, authors explore a quote retrieval task borrowing inspiration from context based recommendation systems (Huang et al, 2012;He et al, 2010). Wang et al (2020) formulate learning to quote as a generation task by using topic modelling (Miao et al, 2017;Wang et al, 2019b) in a sequence-to-sequence network. While previous work has considered idioms, proverbs and common phrases as quotes whereas we specifically work with proverbs.…”