# Dialogue Natural Language Inference

Source: [Dialogue Natural Language Inference](<https://www.sota2.com/research/paper/68fa654f1e209131cea098dd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:23:52.793Z

Authors: Sean Welleck, Jason Weston, Arthur Szlam, Kyunghyun Cho

Published: 2018-11-01T23:10:03.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Verifiable Claim Detection on DIALFACT (test)](<https://www.sota2.com/research/sota/verifiable-claim-detection-on-dialfact-test.md>): Verifiable Claim Detection; DIALFACT (test); Accuracy: 82.1.

- [Claim Verification on DIALFACT (test)](<https://www.sota2.com/research/sota/claim-verification-on-dialfact-test.md>): Claim Verification; DIALFACT (test); Accuracy: 43.3.

- [Claim Verification on DIALFACT (val)](<https://www.sota2.com/research/sota/claim-verification-on-dialfact-val.md>): Claim Verification; DIALFACT (val); Accuracy: 42.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1811.00671>)

[PDF](<https://arxiv.org/pdf/1811.00671.pdf>)