@inproceedings{wang-etal-2020-continuity,
title = "Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations",
author = "Wang, Lingzhi and
Li, Jing and
Zeng, Xingshan and
Zhang, Haisong and
Wong, Kam-Fai",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.538",
doi = "10.18653/v1/2020.emnlp-main.538",
pages = "6640--6650",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations
%A Wang, Lingzhi
%A Li, Jing
%A Zeng, Xingshan
%A Zhang, Haisong
%A Wong, Kam-Fai
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-continuity
%X 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.
%R 10.18653/v1/2020.emnlp-main.538
%U https://aclanthology.org/2020.emnlp-main.538
%U https://doi.org/10.18653/v1/2020.emnlp-main.538
%P 6640-6650
Markdown (Informal)
[Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations](https://aclanthology.org/2020.emnlp-main.538) (Wang et al., EMNLP 2020)
ACL