Datasets:

GEM
/

RiSAWOZ

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


	import json
	import os
	from typing import Dict

	import datasets


	_CITATION = """\
	@inproceedings{quan-etal-2020-risawoz,
	title = "{R}i{SAWOZ}: A Large-Scale Multi-Domain {W}izard-of-{O}z Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling",
	author = "Quan, Jun and
	Zhang, Shian and
	Cao, Qian and
	Li, Zizhong and
	Xiong, Deyi",
	booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
	month = nov,
	year = "2020",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/2020.emnlp-main.67",
	pages = "930--940",
	}
	"""


	_DESCRIPTION = """\
	RiSAWOZ contains 11.2K human-to-human (H2H) multiturn 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.
	"""

	_HOMEPAGE = "https://github.com/terryqj0107/RiSAWOZ"

	_LICENSE = "Attribution 4.0 International (CC BY 4.0) license."

	_EMPTY_BELIEF_STATE = [
	"旅游景点-名称",
	"旅游景点-区域",
	"旅游景点-景点类型",
	"旅游景点-最适合人群",
	"旅游景点-消费",
	"旅游景点-是否地铁直达",
	"旅游景点-门票价格",
	"旅游景点-电话号码",
	"旅游景点-地址",
	"旅游景点-评分",
	"旅游景点-开放时间",
	"旅游景点-特点",
	"餐厅-名称",
	"餐厅-区域",
	"餐厅-菜系",
	"餐厅-价位",
	"餐厅-是否地铁直达",
	"餐厅-人均消费",
	"餐厅-地址",
	"餐厅-电话号码",
	"餐厅-评分",
	"餐厅-营业时间",
	"餐厅-推荐菜",
	"酒店-名称",
	"酒店-区域",
	"酒店-星级",
	"酒店-价位",
	"酒店-酒店类型",
	"酒店-房型",
	"酒店-停车场",
	"酒店-房费",
	"酒店-地址",
	"酒店-电话号码",
	"酒店-评分",
	"电脑-品牌",
	"电脑-产品类别",
	"电脑-分类",
	"电脑-内存容量",
	"电脑-屏幕尺寸",
	"电脑-CPU",
	"电脑-价格区间",
	"电脑-系列",
	"电脑-商品名称",
	"电脑-系统",
	"电脑-游戏性能",
	"电脑-CPU型号",
	"电脑-裸机重量",
	"电脑-显卡类别",
	"电脑-显卡型号",
	"电脑-特性",
	"电脑-色系",
	"电脑-待机时长",
	"电脑-硬盘容量",
	"电脑-价格",
	"火车-出发地",
	"火车-目的地",
	"火车-日期",
	"火车-车型",
	"火车-坐席",
	"火车-车次信息",
	"火车-时长",
	"火车-出发时间",
	"火车-到达时间",
	"火车-票价",
	"飞机-出发地",
	"飞机-目的地",
	"飞机-日期",
	"飞机-舱位档次",
	"飞机-航班信息",
	"飞机-起飞时间",
	"飞机-到达时间",
	"飞机-票价",
	"飞机-准点率",
	"天气-城市",
	"天气-日期",
	"天气-天气",
	"天气-温度",
	"天气-风力风向",
	"天气-紫外线强度",
	"电影-制片国家/地区",
	"电影-类型",
	"电影-年代",
	"电影-主演",
	"电影-导演",
	"电影-片名",
	"电影-主演名单",
	"电影-具体上映时间",
	"电影-片长",
	"电影-豆瓣评分",
	"电视剧-制片国家/地区",
	"电视剧-类型",
	"电视剧-年代",
	"电视剧-主演",
	"电视剧-导演",
	"电视剧-片名",
	"电视剧-主演名单",
	"电视剧-首播时间",
	"电视剧-集数",
	"电视剧-单集片长",
	"电视剧-豆瓣评分",
	"辅导班-班号",
	"辅导班-难度",
	"辅导班-科目",
	"辅导班-年级",
	"辅导班-区域",
	"辅导班-校区",
	"辅导班-上课方式",
	"辅导班-开始日期",
	"辅导班-结束日期",
	"辅导班-每周",
	"辅导班-上课时间",
	"辅导班-下课时间",
	"辅导班-时段",
	"辅导班-课次",
	"辅导班-课时",
	"辅导班-教室地点",
	"辅导班-教师",
	"辅导班-价格",
	"辅导班-课程网址",
	"辅导班-教师网址",
	"汽车-名称",
	"汽车-车型",
	"汽车-级别",
	"汽车-座位数",
	"汽车-车身尺寸(mm)",
	"汽车-厂商",
	"汽车-能源类型",
	"汽车-发动机排量(L)",
	"汽车-发动机马力(Ps)",
	"汽车-驱动方式",
	"汽车-综合油耗(L/100km)",
	"汽车-环保标准",
	"汽车-驾驶辅助影像",
	"汽车-巡航系统",
	"汽车-价格(万元)",
	"汽车-车系",
	"汽车-动力水平",
	"汽车-油耗水平",
	"汽车-倒车影像",
	"汽车-定速巡航",
	"汽车-座椅加热",
	"汽车-座椅通风",
	"汽车-所属价格区间",
	"医院-名称",
	"医院-等级",
	"医院-类别",
	"医院-性质",
	"医院-区域",
	"医院-地址",
	"医院-电话",
	"医院-挂号时间",
	"医院-门诊时间",
	"医院-公交线路",
	"医院-地铁可达",
	"医院-地铁线路",
	"医院-重点科室",
	"医院-CT",
	"医院-3.0T MRI",
	"医院-DSA",
	"通用-产品类别",
	"火车-舱位档次",
	"通用-系列",
	"通用-价格区间",
	"通用-品牌"
	]


	class RiSAWOZ(datasets.GeneratorBasedBuilder):
	"""RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling"""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	features = datasets.Features(
	{
	"dialogue_id": datasets.Value("string"),
	"goal": datasets.Value("string"),
	"domains": datasets.Sequence(datasets.Value("string")),
	"dialogue": datasets.Sequence(
	{
	"turn_id": datasets.Value("int32"),
	"turn_domain": datasets.Sequence(datasets.Value("string")),
	"user_utterance": datasets.Value("string"),
	"system_utterance": datasets.Value("string"),
	"belief_state": {
	"inform slot-values": {
	d: datasets.Value("string") for d in _EMPTY_BELIEF_STATE
	},
	# "inform slot-values": datasets.Value("string"),
	"turn_inform": {
	d: datasets.Value("string") for d in _EMPTY_BELIEF_STATE
	},
	"turn request": datasets.Sequence(datasets.Value("string")),
	},
	"user_actions": datasets.Sequence(
	datasets.Sequence(datasets.Value("string"))
	),
	"system_actions": datasets.Sequence(
	datasets.Sequence(datasets.Value("string"))
	),
	"db_results": datasets.Sequence(datasets.Value("string")),
	"segmented_user_utterance": datasets.Value("string"),
	"segmented_system_utterance": datasets.Value("string"),
	}
	),
	}
	)

	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
	_URL = {"train": "train.json", "test": "test.json", "dev": "dev.json"}

	data_dir = dl_manager.download_and_extract(_URL)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir["train"],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": data_dir["test"], "split": "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir["dev"],
	"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.

	with open(filepath, encoding="utf-8") as f:
	all_data = json.load(f)
	id_ = 0
	for data in all_data:
	for slot in _EMPTY_BELIEF_STATE:
	for dia in data["dialogue"]:
	if slot not in dia["belief_state"]["inform slot-values"]:
	dia["belief_state"]["inform slot-values"][slot] = ""
	if slot not in dia["belief_state"]["turn_inform"]:
	dia["belief_state"]["turn_inform"][slot] = ""

	yield id_, {
	"dialogue_id": data["dialogue_id"],
	"goal": data["goal"],
	"domains": data["domains"],
	"dialogue": data["dialogue"],
	}
	id_ += 1