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
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9771–9787
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.544
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2023.acl-long.544.pdf