论文标题
对话摘要如对话状态（DS2），模板引导的摘要，用于对话态对话状态跟踪
Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking
论文作者
论文摘要
注释以任务为导向的对话对于昂贵且困难的数据收集过程臭名昭著。几乎没有对话状态跟踪（DST）是解决此问题的现实解决方案。在本文中，我们假设对话摘要本质上是非结构化的对话。因此，我们建议将对话状态跟踪作为对话摘要问题进行重新调整。为了详细说明，我们使用基于综合模板的对话摘要来训练文本到文本模型，该模型由对话表明的一组规则产生。然后，可以通过成型应用摘要生成规则来恢复对话状态。我们从经验上表明，在跨域和多域设置中，我们的方法DS2在Multiwoz 2.0和2.1中的几乎没有射击的DST上都优于先前的作品。我们的方法在训练和推理过程中也表现出巨大的加速，因为它可以一次产生所有状态。最后，根据我们的分析，我们发现摘要模板的自然性在成功培训中起着关键作用。
Annotating task-oriented dialogues is notorious for the expensive and difficult data collection process. Few-shot dialogue state tracking (DST) is a realistic solution to this problem. In this paper, we hypothesize that dialogue summaries are essentially unstructured dialogue states; hence, we propose to reformulate dialogue state tracking as a dialogue summarization problem. To elaborate, we train a text-to-text language model with synthetic template-based dialogue summaries, generated by a set of rules from the dialogue states. Then, the dialogue states can be recovered by inversely applying the summary generation rules. We empirically show that our method DS2 outperforms previous works on few-shot DST in MultiWoZ 2.0 and 2.1, in both cross-domain and multi-domain settings. Our method also exhibits vast speedup during both training and inference as it can generate all states at once. Finally, based on our analysis, we discover that the naturalness of the summary templates plays a key role for successful training.
