Datasets:
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
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License:
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# 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
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