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Dialogue NLI dataset

A Dialogue NLI dataset is a natural language processing benchmark designed to evaluate and improve the consistency of conversational agents by framing dialogue coherence as a natural language inference task. In this format, conversational elements, such as persona descriptions, prior dialogue history, and generated utterances, are paired as premises and hypotheses and annotated with relationship labels indicating entailment, contradiction, or neutral standing. By learning these relational dependencies, models trained on such datasets can detect semantic contradictions, verify persona attributes, and ensure that conversational agents maintain coherent, non-contradictory interactions across extended dialogues.

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PAED: Zero-Shot Persona Attribute Extraction in Dialogues

PAED: Zero-Shot Persona Attribute Extraction in Dialogues

Luyao Zhu, Wei Li, Rui Mao, Vlad Pandelea, Erik Cambria

OrganizationsNanyang Technological University

Why you should read this

Introduces a refined persona attribute triplet dataset alongside a generation-based framework that uses a variational autoencoder hard negative sampling strategy to extract structured user personas in zero-shot dialogue settings.

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.

Added

2026-10-04