TY - GEN
T1 - Refocusing on Relevance
T2 - 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021
AU - Dudy, Shiran
AU - Bedrick, Steven
AU - Webber, Bonnie
N1 - Funding Information:
We would like to thank the anonymous reviewers who provided very useful comments. This research was supported by the NSF National AI Institute for Student-AI Teaming (iSAT) under grant DRL 2019805, as well as the National Institute On Deafness And Other Communication Disorders of the National Institutes of Health under Award Number R01DC015999. The opinions expressed are those of the authors, and do not represent views of the NSF or NIH.
Publisher Copyright:
© 2021 Association for Computational Linguistics
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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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M3 - Conference contribution
AN - SCOPUS:85127449695
T3 - EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings
SP - 5190
EP - 5202
BT - EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings
PB - Association for Computational Linguistics (ACL)
Y2 - 7 November 2021 through 11 November 2021
ER -