@inproceedings{dudy-etal-2021-refocusing,
title = "Refocusing on Relevance: Personalization in {NLG}",
author = "Dudy, Shiran and
Bedrick, Steven and
Webber, Bonnie",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.421",
doi = "10.18653/v1/2021.emnlp-main.421",
pages = "5190--5202",
abstract = "Many NLG tasks such as summarization, dialogue response, or open domain question answering, focus primarily on a source text in order to generate a target response. This standard approach falls short, however, when a user{'}s intent or context of work is not easily recoverable based solely on that source text{--} a scenario that we argue is more of the rule than the exception. In this work, we argue that NLG systems in general should place a much higher level of emphasis on making use of additional context, and suggest that relevance (as used in Information Retrieval) be thought of as a crucial tool for designing user-oriented text-generating tasks. We further discuss possible harms and hazards around such personalization, and argue that value-sensitive design represents a crucial path forward through these challenges.",
}
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<abstract>Many NLG tasks such as summarization, dialogue response, or open domain question answering, focus primarily on a source text in order to generate a target response. This standard approach falls short, however, when a user’s intent or context of work is not easily recoverable based solely on that source text– a scenario that we argue is more of the rule than the exception. In this work, we argue that NLG systems in general should place a much higher level of emphasis on making use of additional context, and suggest that relevance (as used in Information Retrieval) be thought of as a crucial tool for designing user-oriented text-generating tasks. We further discuss possible harms and hazards around such personalization, and argue that value-sensitive design represents a crucial path forward through these challenges.</abstract>
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%0 Conference Proceedings
%T Refocusing on Relevance: Personalization in NLG
%A Dudy, Shiran
%A Bedrick, Steven
%A Webber, Bonnie
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F dudy-etal-2021-refocusing
%X Many NLG tasks such as summarization, dialogue response, or open domain question answering, focus primarily on a source text in order to generate a target response. This standard approach falls short, however, when a user’s intent or context of work is not easily recoverable based solely on that source text– a scenario that we argue is more of the rule than the exception. In this work, we argue that NLG systems in general should place a much higher level of emphasis on making use of additional context, and suggest that relevance (as used in Information Retrieval) be thought of as a crucial tool for designing user-oriented text-generating tasks. We further discuss possible harms and hazards around such personalization, and argue that value-sensitive design represents a crucial path forward through these challenges.
%R 10.18653/v1/2021.emnlp-main.421
%U https://aclanthology.org/2021.emnlp-main.421
%U https://doi.org/10.18653/v1/2021.emnlp-main.421
%P 5190-5202
Markdown (Informal)
[Refocusing on Relevance: Personalization in NLG](https://aclanthology.org/2021.emnlp-main.421) (Dudy et al., EMNLP 2021)
ACL
- Shiran Dudy, Steven Bedrick, and Bonnie Webber. 2021. Refocusing on Relevance: Personalization in NLG. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5190–5202, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.