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.
BibTeX
@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"
}