Datasets:
woz_dialogue

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors.
	#
	# 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.
	"""WozDialogue: a dataset for training task-oriented dialogue systems"""


	import json

	import datasets


	_CITATION = """\
	@misc{wen2017networkbased,
	title={A Network-based End-to-End Trainable Task-oriented Dialogue System},
	author={Tsung-Hsien Wen and David Vandyke and Nikola Mrksic and Milica Gasic and Lina M. Rojas-Barahona and Pei-Hao Su and Stefan Ultes and Steve Young},
	year={2017},
	eprint={1604.04562},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """\
	Wizard-of-Oz (WOZ) is a dataset for training task-oriented dialogue systems. The dataset is designed around the \
	task of finding a restaurant in the Cambridge, UK area. There are three informable slots (food, pricerange,area) \
	that users can use to constrain the search and six requestable slots (address, phone, postcode plus the three informable slots) \
	that the user can ask a value for once a restaurant has been offered.
	"""

	_HOMEPAGE = "https://github.com/nmrksic/neural-belief-tracker/tree/master/data/woz"

	_BASE_URL = "https://raw.githubusercontent.com/nmrksic/neural-belief-tracker/master/data/woz"


	class WozDialogue(datasets.GeneratorBasedBuilder):
	"""WozDialogue: a dataset for training task-oriented dialogue systems"""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="en",
	version=datasets.Version("1.0.0"),
	description="WOZ English dataset",
	),
	datasets.BuilderConfig(name="de", version=datasets.Version("1.0.0"), description="WOZ German dataset"),
	datasets.BuilderConfig(
	name="de_en",
	version=datasets.Version("1.0.0"),
	description="WOZ German-English dataset. For this config, the dialogues are in German and the labels in English ",
	),
	datasets.BuilderConfig(name="it", version=datasets.Version("1.0.0"), description="WOZ Italian dataset"),
	datasets.BuilderConfig(
	name="it_en",
	version=datasets.Version("1.0.0"),
	description="WOZ Italian-English dataset. For this config, the dialogues are in Italian and the labels in English ",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"dialogue_idx": datasets.Value("int32"),
	"dialogue": [
	{
	"turn_label": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
	"asr": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
	"system_transcript": datasets.Value("string"),
	"turn_idx": datasets.Value("int32"),
	"belief_state": [
	{
	"slots": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
	"act": datasets.Value("string"),
	}
	],
	"transcript": datasets.Value("string"),
	"system_acts": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
	}
	],
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	urls = {
	"train": f"{_BASE_URL}/woz_train_{self.config.name}.json",
	"dev": f"{_BASE_URL}/woz_validate_{self.config.name}.json",
	"test": f"{_BASE_URL}/woz_test_{self.config.name}.json",
	}
	downloaded_paths = dl_manager.download(urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": downloaded_paths["train"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": downloaded_paths["dev"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": downloaded_paths["test"]},
	),
	]

	def _generate_examples(self, filepath):
	with open(filepath, encoding="utf-8") as f:
	examples = json.load(f)
	for i, example in enumerate(examples):
	for dialogue in example["dialogue"]:
	# exclude the second element which is same for every instance and is of type int
	dialogue["asr"] = [asr[:1] for asr in dialogue["asr"]]
	# some system_acts is either to string or list of strings,
	# converting all to list of strings
	dialogue["system_acts"] = [
	[act] if isinstance(act, str) else act for act in dialogue["system_acts"]
	]

	yield example["dialogue_idx"], example