Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
n<1K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
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. | |
"""Coached Conversational Preference Elicitation Dataset to Understanding Movie Preferences""" | |
import json | |
import os | |
import datasets | |
_CITATION = """\ | |
@inproceedings{48414, | |
title = {Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences}, | |
author = {Filip Radlinski and Krisztian Balog and Bill Byrne and Karthik Krishnamoorthi}, | |
year = {2019}, | |
booktitle = {Proceedings of the Annual SIGdial Meeting on Discourse and Dialogue} | |
} | |
""" | |
_DESCRIPTION = """\ | |
A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing | |
movie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers, | |
where one worker plays the role of an 'assistant', while the other plays the role of a 'user'. The 'assistant' elicits | |
the 'user’s' preferences about movies following a Coached Conversational Preference Elicitation (CCPE) method. The | |
assistant asks questions designed to minimize the bias in the terminology the 'user' employs to convey his or her | |
preferences as much as possible, and to obtain these preferences in natural language. Each dialog is annotated with | |
entity mentions, preferences expressed about entities, descriptions of entities provided, and other statements of | |
entities.""" | |
_HOMEPAGE = "https://research.google/tools/datasets/coached-conversational-preference-elicitation/" | |
_LICENSE = "https://creativecommons.org/licenses/by-sa/4.0/" | |
_URLs = {"dataset": "https://storage.googleapis.com/dialog-data-corpus/CCPE-M-2019/data.json"} | |
class CoachedConvPrefConfig(datasets.BuilderConfig): | |
"""BuilderConfig for DialogRE""" | |
def __init__(self, **kwargs): | |
"""BuilderConfig for DialogRE. | |
Args: | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(CoachedConvPrefConfig, self).__init__(**kwargs) | |
class CoachedConvPref(datasets.GeneratorBasedBuilder): | |
"""Coached Conversational Preference Elicitation Dataset to Understanding Movie Preferences""" | |
VERSION = datasets.Version("1.1.0") | |
BUILDER_CONFIGS = [ | |
CoachedConvPrefConfig( | |
name="coached_conv_pref", | |
version=datasets.Version("1.1.0"), | |
description="Coached Conversational Preference Elicitation Dataset to Understanding Movie Preferences", | |
), | |
] | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"conversationId": datasets.Value("string"), | |
"utterances": datasets.Sequence( | |
{ | |
"index": datasets.Value("int32"), | |
"speaker": datasets.features.ClassLabel(names=["USER", "ASSISTANT"]), | |
"text": datasets.Value("string"), | |
"segments": datasets.Sequence( | |
{ | |
"startIndex": datasets.Value("int32"), | |
"endIndex": datasets.Value("int32"), | |
"text": datasets.Value("string"), | |
"annotations": datasets.Sequence( | |
{ | |
"annotationType": datasets.features.ClassLabel( | |
names=[ | |
"ENTITY_NAME", | |
"ENTITY_PREFERENCE", | |
"ENTITY_DESCRIPTION", | |
"ENTITY_OTHER", | |
] | |
), | |
"entityType": datasets.features.ClassLabel( | |
names=[ | |
"MOVIE_GENRE_OR_CATEGORY", | |
"MOVIE_OR_SERIES", | |
"PERSON", | |
"SOMETHING_ELSE", | |
] | |
), | |
} | |
), | |
} | |
), | |
} | |
), | |
} | |
), | |
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) | |
# Dataset is a single corpus (does not contain any split) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir["dataset"]), | |
"split": "train", | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
"""Yields examples.""" | |
# Empty Segment list with annotations dictionary | |
# First prompt of a conversation does not contain the segment dictionary | |
# We are setting it to None values | |
segments_empty = [ | |
{ | |
"startIndex": 0, | |
"endIndex": 0, | |
"text": "", | |
"annotations": [], | |
} | |
] | |
with open(filepath, encoding="utf-8") as f: | |
dataset = json.load(f) | |
for id_, data in enumerate(dataset): | |
conversationId = data["conversationId"] | |
utterances = data["utterances"] | |
for utterance in utterances: | |
if "segments" not in utterance: | |
utterance["segments"] = segments_empty.copy() | |
yield id_, { | |
"conversationId": conversationId, | |
"utterances": utterances, | |
} | |