Datasets:
Sub-tasks:
dialogue-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
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. | |
"""Taskmaster: A dataset for goal oriented conversations.""" | |
from __future__ import absolute_import, division, print_function | |
import json | |
import datasets | |
_CITATION = """\ | |
@inproceedings{48484, | |
title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset}, | |
author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik}, | |
year = {2019} | |
} | |
""" | |
_DESCRIPTION = """\ | |
Taskmaster is dataset for goal oriented conversations. The Taskmaster-2 dataset consists of 17,289 dialogs \ | |
in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports. \ | |
Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs, \ | |
Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is \ | |
almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs. \ | |
All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced \ | |
workers played the role of a 'user' and trained call center operators played the role of the 'assistant'. \ | |
In this way, users were led to believe they were interacting with an automated system that “spoke” \ | |
using text-to-speech (TTS) even though it was in fact a human behind the scenes. \ | |
As a result, users could express themselves however they chose in the context of an automated interface. | |
""" | |
_HOMEPAGE = "https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020" | |
_BASE_URL = "https://raw.githubusercontent.com/google-research-datasets/Taskmaster/master/TM-2-2020/data" | |
class Taskmaster2(datasets.GeneratorBasedBuilder): | |
"""Taskmaster: A dataset for goal oriented conversations.""" | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="flights", version=datasets.Version("1.0.0"), description="Taskmaster-2 flights domain." | |
), | |
datasets.BuilderConfig( | |
name="food-ordering", version=datasets.Version("1.0.0"), description="Taskmaster-2 food-ordering domain" | |
), | |
datasets.BuilderConfig( | |
name="hotels", version=datasets.Version("1.0.0"), description="Taskmaster-2 hotel domain" | |
), | |
datasets.BuilderConfig( | |
name="movies", version=datasets.Version("1.0.0"), description="Taskmaster-2 movies domain" | |
), | |
datasets.BuilderConfig( | |
name="music", version=datasets.Version("1.0.0"), description="Taskmaster-2 music domain" | |
), | |
datasets.BuilderConfig( | |
name="restaurant-search", | |
version=datasets.Version("1.0.0"), | |
description="Taskmaster-2 restaurant-search domain", | |
), | |
datasets.BuilderConfig( | |
name="sports", version=datasets.Version("1.0.0"), description="Taskmaster-2 sports domain" | |
), | |
] | |
def _info(self): | |
features = { | |
"conversation_id": datasets.Value("string"), | |
"instruction_id": datasets.Value("string"), | |
"utterances": [ | |
{ | |
"index": datasets.Value("int32"), | |
"speaker": datasets.Value("string"), | |
"text": datasets.Value("string"), | |
"segments": [ | |
{ | |
"start_index": datasets.Value("int32"), | |
"end_index": datasets.Value("int32"), | |
"text": datasets.Value("string"), | |
"annotations": [{"name": datasets.Value("string")}], | |
} | |
], | |
} | |
], | |
} | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features(features), | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
url = f"{_BASE_URL}/{self.config.name}.json" | |
dialogs_file = dl_manager.download(url) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={"filepath": dialogs_file}, | |
), | |
] | |
def _generate_examples(self, filepath): | |
with open(filepath, encoding="utf-8") as f: | |
dialogs = json.load(f) | |
for dialog in dialogs: | |
utterances = dialog["utterances"] | |
for utterance in utterances: | |
if "segments" not in utterance: | |
utterance["segments"] = [] | |
yield dialog["conversation_id"], dialog | |