@inproceedings{zhu-etal-2023-paed,
title = "{PAED}: Zero-Shot Persona Attribute Extraction in Dialogues",
author = "Zhu, Luyao and
Li, Wei and
Mao, Rui and
Pandelea, Vlad and
Cambria, Erik",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.544",
doi = "10.18653/v1/2023.acl-long.544",
pages = "9771--9787",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T PAED: Zero-Shot Persona Attribute Extraction in Dialogues
%A Zhu, Luyao
%A Li, Wei
%A Mao, Rui
%A Pandelea, Vlad
%A Cambria, Erik
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhu-etal-2023-paed
%X 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.
%R 10.18653/v1/2023.acl-long.544
%U https://aclanthology.org/2023.acl-long.544
%U https://doi.org/10.18653/v1/2023.acl-long.544
%P 9771-9787
Markdown (Informal)
[PAED: Zero-Shot Persona Attribute Extraction in Dialogues](https://aclanthology.org/2023.acl-long.544) (Zhu et al., ACL 2023)
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.