PAED: Zero-Shot Persona Attribute Extraction in Dialogues
Luyao Zhu, Wei Li, Rui Mao, Vlad Pandelea, Erik Cambria · 2023
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 stateof-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.