# 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