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

Lingzhi Wang, Jing Li, Xingshan Zeng, Haisong Zhang, Kam-Fai Wong


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 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.
Anthology ID:
2020.emnlp-main.538
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6640–6650
Language:
URL:
https://aclanthology.org/2020.emnlp-main.538
DOI:
10.18653/v1/2020.emnlp-main.538
Bibkey:
Cite (ACL):
Lingzhi Wang, Jing Li, Xingshan Zeng, Haisong Zhang, and Kam-Fai Wong. 2020. Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6640–6650, Online. Association for Computational Linguistics.
Cite (Informal):
Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations (Wang et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.538.pdf
Code
 Lingzhi-WANG/Datasets-for-Quotation-Recommendation