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:
File size: 5,404 Bytes
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# 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."""
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):
key = 0
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 key, dialog
key += 1
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