ConvLab/multiwoz21

Dataset Card for MultiWOZ 2.1

• Repository: https://github.com/budzianowski/multiwoz
• Paper: https://aclanthology.org/2020.lrec-1.53
• Leaderboard: https://github.com/budzianowski/multiwoz
• Who transforms the dataset: Qi Zhu(zhuq96 at gmail dot com)

To use this dataset, you need to install ConvLab-3 platform first. Then you can load the dataset via:

from convlab.util import load_dataset, load_ontology, load_database

dataset = load_dataset('multiwoz21')
ontology = load_ontology('multiwoz21')
database = load_database('multiwoz21')

For more usage please refer to here.

Dataset Summary

MultiWOZ 2.1 fixed the noise in state annotations and dialogue utterances. It also includes user dialogue acts from ConvLab (Lee et al., 2019) as well as multiple slot descriptions per dialogue state slot.

• How to get the transformed data from original data:
  • Download MultiWOZ_2.1.zip.
  • Run python preprocess.py in the current directory.
• Main changes of the transformation:
  • Create a new ontology in the unified format, taking slot descriptions from MultiWOZ 2.2.
  • Correct some grammar errors in the text, mainly following tokenization.md in MultiWOZ_2.1.
  • Normalize slot name and value. See normalize_domain_slot_value function in preprocess.py.
  • Correct some non-categorical slots' values and provide character level span annotation.
  • Concatenate multiple values in user goal & state using |.
  • Add booked information in system turns from original belief states.
  • Remove Booking domain and remap all booking relevant dialog acts to unify the annotation of booking action in different domains, see booking_remapper.py.
• Annotations:
  • user goal, dialogue acts, state.

Supported Tasks and Leaderboards

NLU, DST, Policy, NLG, E2E, User simulator

Languages

English

Data Splits

split	dialogues	utterances	avg_utt	avg_tokens	avg_domains	cat slot match(state)	cat slot match(goal)	cat slot match(dialogue act)	non-cat slot span(dialogue act)
train	8438	113556	13.46	13.23	2.8	98.84	99.48	86.39	98.22
validation	1000	14748	14.75	13.5	2.98	98.84	99.46	86.59	98.17
test	1000	14744	14.74	13.5	2.93	99.21	99.32	85.83	98.58
all	10438	143048	13.7	13.28	2.83	98.88	99.47	86.35	98.25

8 domains: ['attraction', 'hotel', 'taxi', 'restaurant', 'train', 'police', 'hospital', 'general']

• cat slot match: how many values of categorical slots are in the possible values of ontology in percentage.
• non-cat slot span: how many values of non-categorical slots have span annotation in percentage.

Citation

@inproceedings{eric-etal-2020-multiwoz,
    title = "{M}ulti{WOZ} 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines",
    author = "Eric, Mihail and Goel, Rahul and Paul, Shachi and Sethi, Abhishek and Agarwal, Sanchit and Gao, Shuyag and Hakkani-Tur, Dilek",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2020.lrec-1.53",
    pages = "422--428",
    ISBN = "979-10-95546-34-4",
}

Licensing Information

Apache License, Version 2.0