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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2021.emnlp-main.421.pdf
- Data
- MIMIC-III