@inproceedings{wang-etal-2022-learning-quote,
title = "Learning When and What to Quote: A Quotation Recommender System with Mutual Promotion of Recommendation and Generation",
author = "Wang, Lingzhi and
Zeng, Xingshan and
Wong, Kam-Fai",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.225",
doi = "10.18653/v1/2022.findings-emnlp.225",
pages = "3094--3105",
abstract = "This work extends the current quotation recommendation task to a more realistic quotation recommender system that learns to predict when to quote and what to quote jointly. The system consists of three modules (tasks), a prediction module to predict whether to quote given conversation contexts, a recommendation module to recommend suitable quotations and a generation module generating quotations or sentences in ordinary language to continue the conversation. We benchmark several competitive models for the two newly introduced tasks (i.e., when-to-quote and what-to-continue). For quotation recommendation, compared with previous work that is either generation-based or ranking-based recommendation, we propose a novel framework with mutual promotion of generation module and ranking-based recommendation module. Experiments show that our framework achieves significantly better performance than baselines on two datasets. Further experiments and analyses validate the effectiveness of the proposed mechanisms and get a better understanding of the quotation recommendation task.",
}
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<abstract>This work extends the current quotation recommendation task to a more realistic quotation recommender system that learns to predict when to quote and what to quote jointly. The system consists of three modules (tasks), a prediction module to predict whether to quote given conversation contexts, a recommendation module to recommend suitable quotations and a generation module generating quotations or sentences in ordinary language to continue the conversation. We benchmark several competitive models for the two newly introduced tasks (i.e., when-to-quote and what-to-continue). For quotation recommendation, compared with previous work that is either generation-based or ranking-based recommendation, we propose a novel framework with mutual promotion of generation module and ranking-based recommendation module. Experiments show that our framework achieves significantly better performance than baselines on two datasets. Further experiments and analyses validate the effectiveness of the proposed mechanisms and get a better understanding of the quotation recommendation task.</abstract>
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%0 Conference Proceedings
%T Learning When and What to Quote: A Quotation Recommender System with Mutual Promotion of Recommendation and Generation
%A Wang, Lingzhi
%A Zeng, Xingshan
%A Wong, Kam-Fai
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F wang-etal-2022-learning-quote
%X This work extends the current quotation recommendation task to a more realistic quotation recommender system that learns to predict when to quote and what to quote jointly. The system consists of three modules (tasks), a prediction module to predict whether to quote given conversation contexts, a recommendation module to recommend suitable quotations and a generation module generating quotations or sentences in ordinary language to continue the conversation. We benchmark several competitive models for the two newly introduced tasks (i.e., when-to-quote and what-to-continue). For quotation recommendation, compared with previous work that is either generation-based or ranking-based recommendation, we propose a novel framework with mutual promotion of generation module and ranking-based recommendation module. Experiments show that our framework achieves significantly better performance than baselines on two datasets. Further experiments and analyses validate the effectiveness of the proposed mechanisms and get a better understanding of the quotation recommendation task.
%R 10.18653/v1/2022.findings-emnlp.225
%U https://aclanthology.org/2022.findings-emnlp.225
%U https://doi.org/10.18653/v1/2022.findings-emnlp.225
%P 3094-3105
Markdown (Informal)
[Learning When and What to Quote: A Quotation Recommender System with Mutual Promotion of Recommendation and Generation](https://aclanthology.org/2022.findings-emnlp.225) (Wang et al., Findings 2022)
ACL