TY - GEN
T1 - Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations
AU - Wang, Lingzhi
AU - Li, Jing
AU - Zeng, Xingshan
AU - Zhang, Haisong
AU - Wong, Kam-fai
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
U2 - 10.18653/v1/2020.emnlp-main.538
DO - 10.18653/v1/2020.emnlp-main.538
M3 - Conference article published in proceeding or book
SP - 6640
EP - 6650
BT - 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
ER -