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Dialogue Natural Language Inference

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arxiv 1811.00671 v2 pith:SAKS6BUE submitted 2018-11-01 cs.CL cs.AI

Sean Welleck , Jason Weston , Arthur Szlam , Kyunghyun Cho This is my paper · ORCID
classification cs.CLcs.AI
keywords dialogueconsistencyinferencelanguagemodelnaturalevaluationmethod
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Consistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natural language inference dataset called Dialogue NLI. We propose a method which demonstrates that a model trained on Dialogue NLI can be used to improve the consistency of a dialogue model, and evaluate the method with human evaluation and with automatic metrics on a suite of evaluation sets designed to measure a dialogue model's consistency.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. When Memory Updates but Behavior Does Not: Repairing Implicit Stale Dependencies in Personalized Agent Responses

    cs.AI 2026-08 conditional novelty 7.0 of 10

    State-to-draft auditing with provenance-verified transitions raises STALE strict-protocol accuracy from .686 to .736, a +5.0 point paired gain led by implicit policy adaptation and premise resistance.

  2. Getting To Know You: User Attribute Extraction from Dialogues

    cs.CL 2019-08 conditional novelty 6.0 of 10

    A two-stage extractor, trained on NLI-generated distant supervision, pulls (subject, predicate, object) user attributes from chit-chat and beats retrieval and generation baselines in human evaluation.

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