Refocusing on Relevance: Personalization in NLG

Shiran Dudy, Steven Bedrick, Bonnie Webber


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.
Anthology ID:
2021.emnlp-main.421
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5190–5202
Language:
URL:
https://aclanthology.org/2021.emnlp-main.421
DOI:
10.18653/v1/2021.emnlp-main.421
Bibkey:
Cite (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.
Cite (Informal):
Refocusing on Relevance: Personalization in NLG (Dudy et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.421.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.421.mp4
Data
MIMIC-III