Work

Dialogue Natural Language Inference

Year: 2019

Type: conference-paper

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.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p19-1363

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

Institutions: New York University, Meta (United States)

Language: English

Topic: Speech and dialogue systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W2962989446

FWCI: 13.38

Cites: 27

Cited by: 249

Related to: 10