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DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue

2020-09-28 18:36:23
Shikib Mehri, Mihail Eric, Dilek Hakkani-Tur

Abstract

A long-standing goal of task-oriented dialogue research is the ability to flexibly adapt dialogue models to new domains. To progress research in this direction, we introduce \textbf{DialoGLUE} (Dialogue Language Understanding Evaluation), a public benchmark consisting of 7 task-oriented dialogue datasets covering 4 distinct natural language understanding tasks, designed to encourage dialogue research in representation-based transfer, domain adaptation, and sample-efficient task learning. We release several strong baseline models, demonstrating performance improvements over a vanilla BERT architecture and state-of-the-art results on 5 out of 7 tasks, by pre-training on a large open-domain dialogue corpus and task-adaptive self-supervised training. Through the DialoGLUE benchmark, the baseline methods, and our evaluation scripts, we hope to facilitate progress towards the goal of developing more general task-oriented dialogue models.

Abstract (translated)

URL

https://arxiv.org/abs/2009.13570

PDF

https://arxiv.org/pdf/2009.13570.pdf


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