Simplifying Dataflow Dialogue Design

TL;DR

This paper advocates for simplifying dataflow dialogue systems by proposing a streamlined annotation format and releasing an execution engine, aiming to boost community engagement and research in this promising approach.

Contribution

It introduces a simplified annotation format and provides an open-source execution engine to facilitate research and experimentation in dataflow dialogue systems.

Findings

Proposed a simplified dataset annotation format.

Released an open-source dataflow execution engine.

Aimed to increase community interest in dataflow dialogue research.

Abstract

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.