Dialogue Natural Language Inference
This addresses consistency issues in dialogue models, which is an incremental improvement for natural language processing applications.
The paper tackles dialogue model consistency by framing it as natural language inference and creating a Dialogue NLI dataset, resulting in a method that improves consistency as evaluated through human and automatic metrics.
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.