PAED: Zero-Shot Persona Attribute Extraction in Dialogues

Luyao Zhu, Wei Li, Rui Mao, Vlad Pandelea, Erik Cambria


Abstract
Persona attribute extraction is critical for personalized human-computer interaction. Dialogue is an important medium that communicates and delivers persona information. Although there is a public dataset for triplet-based persona attribute extraction from conversations, its automatically generated labels present many issues, including unspecific relations and inconsistent annotations. We fix such issues by leveraging more reliable text-label matching criteria to generate high-quality data for persona attribute extraction. We also propose a contrastive learning- and generation-based model with a novel hard negative sampling strategy for generalized zero-shot persona attribute extraction. We benchmark our model with state-of-the-art baselines on our dataset and a public dataset, showing outstanding accuracy gains. Our sampling strategy also exceeds others by a large margin in persona attribute extraction.
Anthology ID:
2023.acl-long.544
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9771–9787
Language:
URL:
https://aclanthology.org/2023.acl-long.544
DOI:
10.18653/v1/2023.acl-long.544
Bibkey:
Cite (ACL):
Luyao Zhu, Wei Li, Rui Mao, Vlad Pandelea, and Erik Cambria. 2023. PAED: Zero-Shot Persona Attribute Extraction in Dialogues. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9771–9787, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
PAED: Zero-Shot Persona Attribute Extraction in Dialogues (Zhu et al., ACL 2023)
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https://preview.aclanthology.org/improve-issue-templates/2023.acl-long.544.pdf
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