论文标题
具有响应选择作为辅助任务的高效面向任务的对话系统
Efficient Task-Oriented Dialogue Systems with Response Selection as an Auxiliary Task
论文作者
论文摘要
在以任务为导向的对话系统中采用预训练的语言模型已导致其文本生成能力的显着增强。但是，由于大量可训练的参数，这些体系结构的使用缓慢，有时可能无法产生各种响应。为了解决这些局限性，我们提出了两个模型，该模型具有用于响应选择的辅助任务 - （1）将干扰因素与地面真相反应区分开，（2）将合成反应与地面真相标签区分开。他们在MultiWoz 2.1数据集上实现最新的结果，其组合得分为107.5和108.3，并且胜过基线，具有三倍的参数。我们发布可再现的代码和检查点，并讨论将辅助任务应用于基于T5的架构的效果。
The adoption of pre-trained language models in task-oriented dialogue systems has resulted in significant enhancements of their text generation abilities. However, these architectures are slow to use because of the large number of trainable parameters and can sometimes fail to generate diverse responses. To address these limitations, we propose two models with auxiliary tasks for response selection - (1) distinguishing distractors from ground truth responses and (2) distinguishing synthetic responses from ground truth labels. They achieve state-of-the-art results on the MultiWOZ 2.1 dataset with combined scores of 107.5 and 108.3 and outperform a baseline with three times more parameters. We publish reproducible code and checkpoints and discuss the effects of applying auxiliary tasks to T5-based architectures.
