# 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. # TODO: Address all TODOs and remove all explanatory comments """JMultiWOZ: Japanese Multi-Domain Wizard-of-Oz dataset for task-oriented dialogue modelling""" import json import os import datasets # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @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 = "", } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ 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. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "https://github.com/nu-dialogue/jmultiwoz" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "CC BY-ND 4.0" # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { "original_zip": "https://github.com/nu-dialogue/jmultiwoz/raw/master/dataset/JMultiWOZ_1.0.zip", } def _flatten_value(values) -> str: if not isinstance(values, list): return values flat_values = [ _flatten_value(v) if isinstance(v, list) else v for v in values ] return "[" + ", ".join(flat_values) + "]" # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case class JMultiWOZDataset(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset features = datasets.Features({ "dialogue_id": datasets.Value("int32"), "dialogue_name": datasets.Value("string"), "system_name": datasets.Value("string"), "user_name": datasets.Value("string"), "goal": datasets.Sequence({ "domain": datasets.Value("string"), "task": datasets.Value("string"), "slot": datasets.Value("string"), "value": datasets.Value("string"), }), "goal_description": datasets.Sequence({ "domain": datasets.Value("string"), "text": datasets.Value("string"), }), "turns": datasets.Sequence({ "turn_id": datasets.Value("int32"), "speaker": datasets.Value("string"), "utterance": datasets.Value("string"), "dialogue_state": { "belief_state": datasets.Sequence({ "domain": datasets.Value("string"), "slot": datasets.Value("string"), "value": datasets.Value("string"), }), "book_state": datasets.Sequence({ "domain": datasets.Value("string"), "slot": datasets.Value("string"), "value": datasets.Value("string"), }), "db_result": { "candidate_entities": datasets.Sequence(datasets.Value("string")), "active_entity": datasets.Sequence({ "slot": datasets.Value("string"), "value": datasets.Value("string"), }) }, "book_result": datasets.Sequence({ "domain": datasets.Value("string"), "success": datasets.Value("string"), "ref": 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, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # 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): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # 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(_URLS["original_zip"]) split_list_path = os.path.join(data_dir, "JMultiWOZ_1.0/split_list.json") dialogues_path = os.path.join(data_dir, "JMultiWOZ_1.0/dialogues.json") with open(split_list_path, "r", encoding="utf-8") as f: split_list = json.load(f) with open(dialogues_path, "r", encoding="utf-8") as f: dialogues = json.load(f) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "dialogues": [dialogues[dialogue_name] for dialogue_name in split_list["train"]], }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "dialogues": [dialogues[dialogue_name] for dialogue_name in split_list["dev"]], }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "dialogues": [dialogues[dialogue_name] for dialogue_name in split_list["test"]], }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, dialogues): # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. for id_, dialogue in enumerate(dialogues): example = { "dialogue_id": dialogue["dialogue_id"], "dialogue_name": dialogue["dialogue_name"], "system_name": dialogue["system_name"], "user_name": dialogue["user_name"], "goal": [], "goal_description": [], "turns": [], } for domain, tasks in dialogue["goal"].items(): for task, slot_values in tasks.items(): if task == "reqt": slot_values = {slot: None for slot in slot_values} for slot, value in slot_values.items(): example["goal"].append({ "domain": domain, "task": task, "slot": slot, "value": value, }) for domain, texts in dialogue["goal_description"].items(): for text in texts: example["goal_description"].append({ "domain": domain, "text": text, }) for turn in dialogue["turns"]: example_turn = { "turn_id": turn["turn_id"], "speaker": turn["speaker"], "utterance": turn["utterance"], "dialogue_state": { "belief_state": [], "book_state": [], "db_result": {}, "book_result": [], }, } if turn["speaker"] == "SYSTEM": for domain, slots in turn["dialogue_state"]["belief_state"].items(): for slot, value in slots.items(): example_turn["dialogue_state"]["belief_state"].append({ "domain": domain, "slot": slot, "value": value, }) for domain, slots in turn["dialogue_state"]["book_state"].items(): for slot, value in slots.items(): example_turn["dialogue_state"]["book_state"].append({ "domain": domain, "slot": slot, "value": value, }) candidate_entities = turn["dialogue_state"]["db_result"]["candidate_entities"] active_entity = turn["dialogue_state"]["db_result"]["active_entity"] if not active_entity: active_entity = {} example_turn["dialogue_state"]["db_result"] = { "candidate_entities":candidate_entities, "active_entity": [{ "slot": slot, "value": _flatten_value(value), } for slot, value in active_entity.items()] } for domain, result in turn["dialogue_state"]["book_result"].items(): example_turn["dialogue_state"]["book_result"].append({ "domain": domain, "success": result["success"], "ref": result["ref"], }) example["turns"].append(example_turn) yield id_, example