Datasets:

GEM
/

CrossWOZ

Languages:

Chinese

Tags:

dialog-response-generation

License:

Dataset card Files Files and versions Community

CrossWOZ / CrossWOZ.py

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	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset"""


	import json
	import os

	import datasets


	_CITATION = """\
	@article{zhu2020crosswoz,
	author = {Qi Zhu and Kaili Huang and Zheng Zhang and Xiaoyan Zhu and Minlie Huang},
	title = {Cross{WOZ}: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset},
	journal = {Transactions of the Association for Computational Linguistics},
	year = {2020}
	}
	"""

	_DESCRIPTION = """\
	CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. \
	It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, \
	restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of \
	dialogue states and dialogue acts at both user and system sides.
	"""

	_HOMEPAGE = "https://github.com/thu-coai/CrossWOZ"

	_LICENSE = "Apache License, Version 2.0"


	class CrossWOZ(datasets.GeneratorBasedBuilder):
	"""CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset"""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	features = datasets.Features(
	{
	"gem_id": datasets.Value("string"),
	"dialog_id": datasets.Value("string"),
	"sys_id": datasets.Value("int32"),
	"usr_id": datasets.Value("int32"),
	"goal": datasets.Sequence(
	{
	"sub_goal_id": datasets.Value("int32"),
	"domain": datasets.Value("string"),
	"slot": datasets.Value("string"),
	"value": datasets.Value("string"),
	"has_mentioned": datasets.Value("bool"),
	}
	),
	"task description": datasets.Value("string"),
	"type": datasets.Value("string"),
	"messages": datasets.Sequence(
	{
	"content": datasets.Value("string"),
	"role": datasets.Value("string"),
	"dialog_act": datasets.Sequence(
	{
	"intent": datasets.Value("string"),
	"domain": datasets.Value("string"),
	"slot": datasets.Value("string"),
	"value": datasets.Value("string"),
	}
	),
	"user_state": datasets.Sequence(
	{
	"sub_goal_id": datasets.Value("int32"),
	"domain": datasets.Value("string"),
	"slot": datasets.Value("string"),
	"value": datasets.Value("string"),
	"has_mentioned": datasets.Value("bool"),
	}
	),
	"sys_state": {
	"景点": {
	"名称": datasets.Value("string"),
	"门票": datasets.Value("string"),
	"游玩时间": datasets.Value("string"),
	"评分": datasets.Value("string"),
	"周边景点": datasets.Value("string"),
	"周边餐馆": datasets.Value("string"),
	"周边酒店": datasets.Value("string"),
	"selectedResults": datasets.Sequence(datasets.Value("string"))
	},
	"餐馆": {
	"名称": datasets.Value("string"),
	"推荐菜": datasets.Value("string"),
	"人均消费": datasets.Value("string"),
	"评分": datasets.Value("string"),
	"周边景点": datasets.Value("string"),
	"周边餐馆": datasets.Value("string"),
	"周边酒店": datasets.Value("string"),
	"selectedResults": datasets.Sequence(datasets.Value("string"))
	},
	"酒店": {
	"名称": datasets.Value("string"),
	"酒店类型": datasets.Value("string"),
	"酒店设施": datasets.Value("string"),
	"价格": datasets.Value("string"),
	"评分": datasets.Value("string"),
	"周边景点": datasets.Value("string"),
	"周边餐馆": datasets.Value("string"),
	"周边酒店": datasets.Value("string"),
	"selectedResults": datasets.Sequence(datasets.Value("string"))
	},
	"地铁": {
	"出发地": datasets.Value("string"),
	"目的地": datasets.Value("string"),
	"selectedResults": datasets.Sequence(datasets.Value("string"))
	},
	"出租": {
	"出发地": datasets.Value("string"),
	"目的地": datasets.Value("string"),
	"selectedResults": datasets.Sequence(datasets.Value("string"))
	}
	},
	"sys_state_init": {
	"景点": {
	"名称": datasets.Value("string"),
	"门票": datasets.Value("string"),
	"游玩时间": datasets.Value("string"),
	"评分": datasets.Value("string"),
	"周边景点": datasets.Value("string"),
	"周边餐馆": datasets.Value("string"),
	"周边酒店": datasets.Value("string"),
	"selectedResults": datasets.Sequence(datasets.Value("string"))
	},
	"餐馆": {
	"名称": datasets.Value("string"),
	"推荐菜": datasets.Value("string"),
	"人均消费": datasets.Value("string"),
	"评分": datasets.Value("string"),
	"周边景点": datasets.Value("string"),
	"周边餐馆": datasets.Value("string"),
	"周边酒店": datasets.Value("string"),
	"selectedResults": datasets.Sequence(datasets.Value("string"))
	},
	"酒店": {
	"名称": datasets.Value("string"),
	"酒店类型": datasets.Value("string"),
	"酒店设施": datasets.Value("string"),
	"价格": datasets.Value("string"),
	"评分": datasets.Value("string"),
	"周边景点": datasets.Value("string"),
	"周边餐馆": datasets.Value("string"),
	"周边酒店": datasets.Value("string"),
	"selectedResults": datasets.Sequence(datasets.Value("string"))
	},
	"地铁": {
	"出发地": datasets.Value("string"),
	"目的地": datasets.Value("string"),
	"selectedResults": datasets.Sequence(datasets.Value("string"))
	},
	"出租": {
	"出发地": datasets.Value("string"),
	"目的地": datasets.Value("string"),
	"selectedResults": datasets.Sequence(datasets.Value("string"))
	}
	},
	}
	),
	"final_goal": datasets.Sequence(
	{
	"sub_goal_id": datasets.Value("int32"),
	"domain": datasets.Value("string"),
	"slot": datasets.Value("string"),
	"value": datasets.Value("string"),
	"has_mentioned": datasets.Value("bool"),
	}
	)
	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	data_dir = dl_manager.download_and_extract("data.zip")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "train.json"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "test.json"),
	"split": "test"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "val.json"),
	"split": "dev",
	},
	),
	]

	def _generate_examples(
	self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	):
	""" Yields examples as (key, example) tuples. """
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is here for legacy reason (tfds) and is not important in itself.
	def convert_goal(raw_goal):
	goal = []
	for subgoal in raw_goal:
	goal.append({
	"sub_goal_id": subgoal[0],
	"domain": subgoal[1],
	"slot": subgoal[2],
	"value": str(subgoal[3]),
	"has_mentioned": subgoal[4],
	})
	return goal

	key = 0
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)
	for dialog_id, dialog in data.items():
	messages = []
	for turn in dialog["messages"]:
	dialog_act = []
	for da in turn["dialog_act"]:
	dialog_act.append({
	"intent": da[0],
	"domain": da[1],
	"slot": da[2],
	"value": da[3],
	})
	turn["dialog_act"] = dialog_act
	if "user_state" not in turn:
	turn["user_state"] = []
	else:
	turn["user_state"] = convert_goal(turn["user_state"])
	if "sys_state" not in turn:
	turn["sys_state"] = {}
	if "sys_state_init" not in turn:
	turn["sys_state_init"] = {}
	messages.append(turn)

	yield key, {
	"gem_id": f"{self.config.name}-{split}-{key}",
	"dialog_id": dialog_id,
	"sys_id": dialog["sys-usr"][0],
	"usr_id": dialog["sys-usr"][1],
	"goal": convert_goal(dialog["goal"]),
	"task description": dialog["task description"],
	"type": dialog["type"],
	"messages": messages,
	"final_goal": convert_goal(dialog["final_goal"])
	}
	key += 1