论文标题
简化数据流对话设计
Simplifying Dataflow Dialogue Design
论文作者
论文摘要
在\ citep {andreas202020task面向}中，引入了基于数据流（DF）的对话系统，与许多常用的当前系统相比，具有明显的优势。这伴随着Smcalflow的发布，Smcalflow是一个实际上相关的，手动注释的数据集，比任何可比的对话数据集更详细且大得多。尽管有这些出色的贡献，但社区并未对这一方向表现出进一步的兴趣。这种缺乏兴趣的原因是什么？如何鼓励社区朝这个方向进行研究？ 一种解释可能是这种方法过于复杂的看法 - 注释和系统。本文认为，这种感知是错误的：1）提出了简化格式的数据集注释的建议，2）释放DF执行引擎的实现\ footNote {https://github.com/telepathylybathylabsai/opendf}，可以作为砂盒作为砂盒，可以轻松地进行研究，并允许使用砂盒，并实验了实验。希望这些贡献将帮助更多的从业者探索基于DF的对话系统的新想法和设计。
In \citep{andreas2020task-oriented}, a dataflow (DF) based dialogue system was introduced, showing clear advantages compared to many commonly used current systems. This was accompanied by the release of SMCalFlow, a practically relevant, manually annotated dataset, more detailed and much larger than any comparable dialogue dataset. Despite these remarkable contributions, the community has not shown further interest in this direction. What are the reasons for this lack of interest? And how can the community be encouraged to engage in research in this direction? One explanation may be the perception that this approach is too complex - both the the annotation and the system. This paper argues that this perception is wrong: 1) Suggestions for a simplified format for the annotation of the dataset are presented, 2) An implementation of the DF execution engine is released\footnote{https://github.com/telepathylabsai/OpenDF}, which can serve as a sandbox allowing researchers to easily implement, and experiment with, new DF dialogue designs. The hope is that these contributions will help engage more practitioners in exploring new ideas and designs for DF based dialogue systems.
