metadata
license: cc-by-sa-4.0
task_categories:
- text-generation
- text-classification
language:
- ja
pretty_name: Japanese Multi-domain Wizard-of-Oz
size_categories:
- 1K<n<10K
task_ids:
- dialogue-modeling
- parsing
multilinguality:
- monolingual
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
source_datasets:
- original
dataset_info:
features:
- name: dialogue_id
dtype: int32
- name: dialogue_name
dtype: string
- name: system_name
dtype: string
- name: user_name
dtype: string
- name: goal
sequence:
- name: domain
dtype: string
- name: task
dtype: string
- name: slot
dtype: string
- name: value
dtype: string
- name: goal_description
sequence:
- name: domain
dtype: string
- name: text
dtype: string
- name: turns
sequence:
- name: turn_id
dtype: int32
- name: speaker
dtype: string
- name: utterance
dtype: string
- name: dialogue_state
struct:
- name: belief_state
sequence:
- name: domain
dtype: string
- name: slot
dtype: string
- name: value
dtype: string
- name: book_state
sequence:
- name: domain
dtype: string
- name: slot
dtype: string
- name: value
dtype: string
- name: db_result
struct:
- name: candidate_entities
sequence:
dtype: string
id: entity_name
id: candidate_entities
- name: active_entity
sequence:
- name: slot
dtype: string
id: active_entity/slot
- name: value
dtype: string
id: active_entity/value
- name: book_result
sequence:
- name: domain
dtype: string
- name: success
dtype: string
- name: ref
dtype: string
splits:
- name: train
num_bytes: 60731411
num_examples: 3646
- name: validation
num_bytes: 5000420
num_examples: 300
- name: test
num_bytes: 5085276
num_examples: 300
download_size: 11016438
dataset_size: 70817107
Dataset Card for JMultiWOZ
Dataset Description
- Repository: nu-dialouge/jmultiwoz
- Paper: JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset
- Point of Contact: Atsumoto Ohashi
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 = "",
}