@inproceedings{chang-etal-2026-makes,
title = "What Makes an Ideal Quote? Recommending ``Unexpected yet Rational'' Quotations via Novelty",
author = "Chang, Powei and
Xiao, Jin and
Yue, Guanglei and
He, Qianyu and
Xiao, Yanghua and
Yang, Deqing and
Liang, Jiaqing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.362/",
pages = "7985--8017",
ISBN = "979-8-89176-390-6",
abstract = "Quotation recommendation enriches writing by suggesting quotations that fit a given context, but prior systems largely focus on topical relevance and overlook what makes quotes memorable. Based on a user study, we find that preferred quotations are often unexpected yet rational, motivating the goal of selecting quotes that are contextually novel while semantically coherent. We propose NovelQR, which (1) uses a generative label agent to map quotations and contexts into multi-dimensional deep-meaning labels for label-enhanced retrieval, and (2) reranks candidates with a token-level novelty estimator that mitigates auto-regressive continuation bias. Experiments on bilingual datasets across diverse domains show that NovelQR is preferred by human judges and improves overall recommendation quality over strong baselines, while achieving competitive novelty estimation."
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<abstract>Quotation recommendation enriches writing by suggesting quotations that fit a given context, but prior systems largely focus on topical relevance and overlook what makes quotes memorable. Based on a user study, we find that preferred quotations are often unexpected yet rational, motivating the goal of selecting quotes that are contextually novel while semantically coherent. We propose NovelQR, which (1) uses a generative label agent to map quotations and contexts into multi-dimensional deep-meaning labels for label-enhanced retrieval, and (2) reranks candidates with a token-level novelty estimator that mitigates auto-regressive continuation bias. Experiments on bilingual datasets across diverse domains show that NovelQR is preferred by human judges and improves overall recommendation quality over strong baselines, while achieving competitive novelty estimation.</abstract>
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%0 Conference Proceedings
%T What Makes an Ideal Quote? Recommending “Unexpected yet Rational” Quotations via Novelty
%A Chang, Powei
%A Xiao, Jin
%A Yue, Guanglei
%A He, Qianyu
%A Xiao, Yanghua
%A Yang, Deqing
%A Liang, Jiaqing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F chang-etal-2026-makes
%X Quotation recommendation enriches writing by suggesting quotations that fit a given context, but prior systems largely focus on topical relevance and overlook what makes quotes memorable. Based on a user study, we find that preferred quotations are often unexpected yet rational, motivating the goal of selecting quotes that are contextually novel while semantically coherent. We propose NovelQR, which (1) uses a generative label agent to map quotations and contexts into multi-dimensional deep-meaning labels for label-enhanced retrieval, and (2) reranks candidates with a token-level novelty estimator that mitigates auto-regressive continuation bias. Experiments on bilingual datasets across diverse domains show that NovelQR is preferred by human judges and improves overall recommendation quality over strong baselines, while achieving competitive novelty estimation.
%U https://aclanthology.org/2026.acl-long.362/
%P 7985-8017
Markdown (Informal)
[What Makes an Ideal Quote? Recommending “Unexpected yet Rational” Quotations via Novelty](https://aclanthology.org/2026.acl-long.362/) (Chang et al., ACL 2026)
ACL
- Powei Chang, Jin Xiao, Guanglei Yue, Qianyu He, Yanghua Xiao, Deqing Yang, and Jiaqing Liang. 2026. What Makes an Ideal Quote? Recommending “Unexpected yet Rational” Quotations via Novelty. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7985–8017, San Diego, California, United States. Association for Computational Linguistics.