MultiWOZ-DF -- A Dataflow implementation of the MultiWOZ dataset

TL;DR

This paper presents a dataflow-based implementation for the MultiWOZ dataset, enabling dialogue modeling with hierarchical computational graphs, and evaluates different conversion methods and their accuracy.

Contribution

It introduces a dataflow implementation for MultiWOZ, provides multiple conversion approaches, and reports experimental results on accuracy and state matching.

Findings

Multiple conversion methods evaluated

Achieved measurable translation accuracy

Implementation enables execution of MultiWOZ dialogues

Abstract

Semantic Machines (SM) have introduced the use of the dataflow (DF) paradigm to dialogue modelling, using computational graphs to hierarchically represent user requests, data, and the dialogue history [Semantic Machines et al. 2020]. Although the main focus of that paper was the SMCalFlow dataset (to date, the only dataset with "native" DF annotations), they also reported some results of an experiment using a transformed version of the commonly used MultiWOZ dataset [Budzianowski et al. 2018] into a DF format. In this paper, we expand the experiments using DF for the MultiWOZ dataset, exploring some additional experimental set-ups. The code and instructions to reproduce the experiments reported here have been released. The contributions of this paper are: 1.) A DF implementation capable of executing MultiWOZ dialogues; 2.) Several versions of conversion of MultiWOZ into a DF format are presented; 3.) Experimental results on state match and translation accuracy.