# 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