Datasets:
meta_woz

	# 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
	#
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	# distributed under the License is distributed on an "AS IS" BASIS,
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	"""MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models"""


	import json
	import os

	import datasets


	_CITATION = """\
	@InProceedings{shalyminov2020fast,
	author = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes},
	title = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer},
	booktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	year = {2020},
	month = {April},
	url = {https://www.microsoft.com/en-us/research/publication/fast-domain-adaptation-for-goal-oriented-dialogue-using-a
	-hybrid-generative-retrieval-transformer/},
	}
	"""

	_DESCRIPTION = """\
	MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models. \
	We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for \
	conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to \
	quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas \
	of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two \
	human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human \
	user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a \
	particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total. \
	Dialogues are a minimum of 10 turns long.
	"""

	_HOMEPAGE = "https://www.microsoft.com/en-us/research/project/metalwoz/"

	_LICENSE = "Microsoft Research Data License Agreement"

	_URLs = {
	"train": "https://download.microsoft.com/download/E/B/8/EB84CB1A-D57D-455F-B905-3ABDE80404E5/metalwoz-v1.zip",
	"test": "https://download.microsoft.com/download/0/c/4/0c4a8893-cbf9-4a43-a44a-09bab9539234/metalwoz-test-v1.zip",
	}


	class MetaWoz(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="dialogues", description="The dataset of dialogues from various domains."),
	datasets.BuilderConfig(
	name="tasks", description="The metadata for tasks corresponding to dialogues from " "various domains."
	),
	]

	DEFAULT_CONFIG_NAME = "dialogues"

	def _info(self):
	if self.config.name == "tasks":
	features = datasets.Features(
	{
	"task_id": datasets.Value("string"),
	"domain": datasets.Value("string"),
	"bot_prompt": datasets.Value("string"),
	"bot_role": datasets.Value("string"),
	"user_prompt": datasets.Value("string"),
	"user_role": datasets.Value("string"),
	}
	)
	else:
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"user_id": datasets.Value("string"),
	"bot_id": datasets.Value("string"),
	"domain": datasets.Value("string"),
	"task_id": datasets.Value("string"),
	"turns": datasets.Sequence(datasets.Value("string")),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URLs)
	data_dir["test"] = dl_manager.extract(os.path.join(data_dir["test"], "dstc8_metalwoz_heldout.zip"))

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

	def _generate_examples(self, data_dir):
	""" Yields examples. """
	if self.config.name == "tasks":
	filepath = os.path.join(data_dir, "tasks.txt")
	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	yield id_, {
	"task_id": data["task_id"],
	"domain": data["domain"],
	"bot_prompt": data["bot_prompt"],
	"bot_role": data["bot_role"],
	"user_prompt": data["user_prompt"],
	"user_role": data["user_role"],
	}
	else:
	id_ = -1
	base_path = os.path.join(data_dir, "dialogues")
	file_list = sorted(
	[os.path.join(base_path, file) for file in os.listdir(base_path) if file.endswith(".txt")]
	)
	for filepath in file_list:
	with open(filepath, encoding="utf-8") as f:
	for row in f:
	id_ += 1
	data = json.loads(row)
	yield id_, {
	"id": data["id"],
	"user_id": data["user_id"],
	"bot_id": data["bot_id"],
	"domain": data["domain"],
	"task_id": data["task_id"],
	"turns": data["turns"],
	}