论文标题
对话：一种自然语言理解为任务对话的基准
DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue
论文作者
论文摘要
以任务为导向对话研究的长期目标是能够灵活地将对话模型适应新领域的能力。为了朝这个方向进行研究，我们介绍对话（对话语言理解评估），这是一个由7个面向任务的对话数据集组成的公共基准，涵盖了4个不同的自然语言理解任务，旨在鼓励基于表示的转移，域适应性和样本有效的任务学习。我们发布了几种强大的基线模型，通过对大型开放域对话语料库进行预训练和任务自适应的自我监督培训，证明了7个任务中的5个任务中的5个任务中的5个任务中的5个任务结果改进了性能。通过对DialogLue基准，基线方法和我们的评估脚本，我们希望促进进步的目标，以开发更一般的任务对话模型。
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 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.
