MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines

TL;DR

This paper introduces MultiWOZ 2.2, an improved dialogue dataset with corrected annotations, redefined ontology, and standardized slot span annotations, along with baseline benchmarks for dialogue state tracking models.

Contribution

It provides a refined version of MultiWOZ with extensive annotation corrections, ontology updates, and standardized slot annotations, plus baseline model evaluations.

Findings

Reduced annotation errors in 17.3% of dialogues

Standardized slot span annotations for key slots

Benchmark results for state-of-the-art dialogue models

Abstract

MultiWOZ is a well-known task-oriented dialogue dataset containing over 10,000 annotated dialogues spanning 8 domains. It is extensively used as a benchmark for dialogue state tracking. However, recent works have reported presence of substantial noise in the dialogue state annotations. MultiWOZ 2.1 identified and fixed many of these erroneous annotations and user utterances, resulting in an improved version of this dataset. This work introduces MultiWOZ 2.2, which is a yet another improved version of this dataset. Firstly, we identify and fix dialogue state annotation errors across 17.3% of the utterances on top of MultiWOZ 2.1. Secondly, we redefine the ontology by disallowing vocabularies of slots with a large number of possible values (e.g., restaurant name, time of booking). In addition, we introduce slot span annotations for these slots to standardize them across recent models, which previously used custom string matching heuristics to generate them. We also benchmark a few state of the art dialogue state tracking models on the corrected dataset to facilitate comparison for future work. In the end, we discuss best practices for dialogue data collection that can help avoid annotation errors.