Datasets:
Sub-tasks:
dialogue-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
Tags:
License:
# 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. | |
"""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"], | |
} | |