Zhu, L., Li, W., Mao, R., Pandelea, V., Cambria, E. (2023). PAED: Zero-Shot Persona Attribute Extraction in Dialogues. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/2023.acl-long.544
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - AME Programmatic Grant
Grant Reference no. : A18A2b0046