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Dataset Card for JMultiWOZ
Dataset Summary
JMultiWOZ is a large-scale Japanese multi-domain task-oriented dialogue dataset. The dataset is collected using the Wizard-of-Oz (WoZ) methodology, where two human annotators simulate the user and the system. The dataset contains 4,246 dialogues across 6 domains, including restaurant, hotel, attraction, shopping, taxi, and weather. Available annotations include user goal, dialogue state, and utterances.
Supported Tasks
- Dialogue State Tracking: The dataset can be used to train models for dialogue state tracking, which is the task of predicting the user's belief state at each turn in the dialogue.
- Dialogue Generation: The dataset can be used to train models for dialogue generation, which is the task of generating a response given the dialogue history.
Languages
The text in the dataset is in Japanese (ja
).
Dataset Usage
from datasets import load_dataset
dataset = load_dataset("nu-dialogue/jmultiwoz", trust_remote_code=True)
Dataset Structure
Data Instances
A data instance is a full multi-turn dialogue between a USER
and a SYSTEM
. Each turn has an utterance
:
[
"福岡へ行くよていなのですが、値段が普通くらいの宿泊施設を探してもらっていいですか?",
"かしこまりました。ではWITH THE STYLE FUKUOKAはいかがでしょうか。"
]
SYSTEM
turn also has a dialogue_state
which contains belief_state
, book_state
, db_result
, and book_result
:
belief_state
:
{
"domain": ["general", "general", "hotel", ...],
"slot": ["active_domain", "city", "pricerange", ...],
"value": ["hotel", "福岡", "普通", ...]
}
book_state
:
{
"domain": ["hotel", "hotel", "hotel", ...],
"slot": ["people", "day", "stay", ...],
"value": [None, None, None, ...]
}
db_result
:
{
"candidate_entities": ["WITH THE STYLE FUKUOKA", "ANA クラウンプラザホテル福岡", ...],
"active_entity": {
"slot": ["city", "name", "genre", ...],
"value": ["福岡", "WITH THE STYLE FUKUOKA", "リゾートホテル", ...]
}
Data Fields
Each dialogue instance has the following fields:
dialogue_id
(int32): A unique identifier for the dialogue.dialogue_name
(string): A name for the dialogue.system_name
(string): The name of the wizard.user_name
(string): The name of the user.goal
(sequence): The user's goal for the dialogue.domain
(string): The domain of the goal.task
(string): The task of the goal.slot
(string): The slot of the goal.value
(string): The value of the goal.
goal_description
(sequence): A description of the user's goal.domain
(string): The domain of the goal.text
(string): The text of the goal.
turns
(sequence): The turns in the dialogue.turn_id
(int32): A unique identifier for the turn.speaker
(string): The speaker of the turn.utterance
(string): The utterance of the turn.dialogue_state
(struct): The dialogue state of the turn.belief_state
(sequence): The belief state of the turn.domain
(string): The domain of the belief state.slot
(string): The slot of the belief state.value
(string): The value of the belief state.
book_state
(sequence): The book state of the turn.domain
(string): The domain of the book state.slot
(string): The slot of the book state.value
(string): The value of the book state.
db_result
(struct): The database result of the turn.candidate_entities
(sequence): The candidate entities of the database result.entity_name
(string): The name of the entity.
active_entity
(sequence): The active entity of the database result.slot
(string): The slot of the active entity.value
(string): The value of the active entity.
book_result
(sequence): The book result of the turn.domain
(string): The domain of the book result.success
(string): The success of the book result.ref
(string): The reference of the book result.
Data Splits
The dataset is split into a train, validation, and test split with the following sizes:
train | validation | test | |
---|---|---|---|
Number of dialogues | 3646 | 300 | 300 |
Number of turns | 52,405 | 4,346 | 4,435 |
Citation Information
@inproceedings{ohashi-etal-2024-jmultiwoz,
title = "JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset",
author = "Ohashi, Atsumoto and Hirai, Ryu and Iizuka, Shinya and Higashinaka, Ryuichiro",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation",
year = "2024",
url = "",
pages = "",
}
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