RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling

TL;DR

RiSAWOZ is a large-scale, richly annotated Chinese multi-domain dialogue dataset designed to advance task-oriented dialogue modeling, including coreference and ellipsis resolution, with benchmark results for various dialogue tasks.

Contribution

This paper introduces RiSAWOZ, the largest multi-domain Chinese Wizard-of-Oz dataset with comprehensive semantic and discourse annotations, filling a data gap for dialogue research.

Findings

Contains 11.2K dialogues with 150K utterances across 12 domains.

Provides detailed annotations including dialogue goals, states, acts, and discourse phenomena.

Benchmark results for intent detection, slot filling, state tracking, and coreference resolution.

Abstract

In order to alleviate the shortage of multi-domain data and to capture discourse phenomena for task-oriented dialogue modeling, we propose RiSAWOZ, a large-scale multi-domain Chinese Wizard-of-Oz dataset with Rich Semantic Annotations. RiSAWOZ contains 11.2K human-to-human (H2H) multi-turn semantically annotated dialogues, with more than 150K utterances spanning over 12 domains, which is larger than all previous annotated H2H conversational datasets. Both single- and multi-domain dialogues are constructed, accounting for 65% and 35%, respectively. Each dialogue is labeled with comprehensive dialogue annotations, including dialogue goal in the form of natural language description, domain, dialogue states and acts at both the user and system side. In addition to traditional dialogue annotations, we especially provide linguistic annotations on discourse phenomena, e.g., ellipsis and coreference, in dialogues, which are useful for dialogue coreference and ellipsis resolution tasks. Apart from the fully annotated dataset, we also present a detailed description of the data collection procedure, statistics and analysis of the dataset. A series of benchmark models and results are reported, including natural language understanding (intent detection & slot filling), dialogue state tracking and dialogue context-to-text generation, as well as coreference and ellipsis resolution, which facilitate the baseline comparison for future research on this corpus.