google-research-datasets/coached_conv_pref

1# coding=utf-82# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.3#4# Licensed under the Apache License, Version 2.0 (the "License");5# you may not use this file except in compliance with the License.6# You may obtain a copy of the License at7#8#     http://www.apache.org/licenses/LICENSE-2.09#10# Unless required by applicable law or agreed to in writing, software11# distributed under the License is distributed on an "AS IS" BASIS,12# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.13# See the License for the specific language governing permissions and14# limitations under the License.15"""Coached Conversational Preference Elicitation Dataset to Understanding Movie Preferences"""16 17 18import json19import os20 21import datasets22 23 24_CITATION = """\25@inproceedings{48414,26title	= {Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences},27author	= {Filip Radlinski and Krisztian Balog and Bill Byrne and Karthik Krishnamoorthi},28year	= {2019},29booktitle	= {Proceedings of the Annual SIGdial Meeting on Discourse and Dialogue}30}31"""32 33_DESCRIPTION = """\34A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing35movie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers,36where one worker plays the role of an 'assistant', while the other plays the role of a 'user'. The 'assistant' elicits37the 'user’s' preferences about movies following a Coached Conversational Preference Elicitation (CCPE) method. The38assistant asks questions designed to minimize the bias in the terminology the 'user' employs to convey his or her39preferences as much as possible, and to obtain these preferences in natural language. Each dialog is annotated with40entity mentions, preferences expressed about entities, descriptions of entities provided, and other statements of41entities."""42 43_HOMEPAGE = "https://research.google/tools/datasets/coached-conversational-preference-elicitation/"44 45_LICENSE = "https://creativecommons.org/licenses/by-sa/4.0/"46 47_URLs = {"dataset": "https://storage.googleapis.com/dialog-data-corpus/CCPE-M-2019/data.json"}48 49 50class CoachedConvPrefConfig(datasets.BuilderConfig):51    """BuilderConfig for DialogRE"""52 53    def __init__(self, **kwargs):54        """BuilderConfig for DialogRE.55        Args:56          **kwargs: keyword arguments forwarded to super.57        """58        super(CoachedConvPrefConfig, self).__init__(**kwargs)59 60 61class CoachedConvPref(datasets.GeneratorBasedBuilder):62    """Coached Conversational Preference Elicitation Dataset to Understanding Movie Preferences"""63 64    VERSION = datasets.Version("1.1.0")65 66    BUILDER_CONFIGS = [67        CoachedConvPrefConfig(68            name="coached_conv_pref",69            version=datasets.Version("1.1.0"),70            description="Coached Conversational Preference Elicitation Dataset to Understanding Movie Preferences",71        ),72    ]73 74    def _info(self):75        return datasets.DatasetInfo(76            description=_DESCRIPTION,77            features=datasets.Features(78                {79                    "conversationId": datasets.Value("string"),80                    "utterances": datasets.Sequence(81                        {82                            "index": datasets.Value("int32"),83                            "speaker": datasets.features.ClassLabel(names=["USER", "ASSISTANT"]),84                            "text": datasets.Value("string"),85                            "segments": datasets.Sequence(86                                {87                                    "startIndex": datasets.Value("int32"),88                                    "endIndex": datasets.Value("int32"),89                                    "text": datasets.Value("string"),90                                    "annotations": datasets.Sequence(91                                        {92                                            "annotationType": datasets.features.ClassLabel(93                                                names=[94                                                    "ENTITY_NAME",95                                                    "ENTITY_PREFERENCE",96                                                    "ENTITY_DESCRIPTION",97                                                    "ENTITY_OTHER",98                                                ]99                                            ),100                                            "entityType": datasets.features.ClassLabel(101                                                names=[102                                                    "MOVIE_GENRE_OR_CATEGORY",103                                                    "MOVIE_OR_SERIES",104                                                    "PERSON",105                                                    "SOMETHING_ELSE",106                                                ]107                                            ),108                                        }109                                    ),110                                }111                            ),112                        }113                    ),114                }115            ),116            supervised_keys=None,117            homepage=_HOMEPAGE,118            license=_LICENSE,119            citation=_CITATION,120        )121 122    def _split_generators(self, dl_manager):123        """Returns SplitGenerators."""124 125        data_dir = dl_manager.download_and_extract(_URLs)126 127        # Dataset is a single corpus (does not contain any split)128        return [129            datasets.SplitGenerator(130                name=datasets.Split.TRAIN,131                gen_kwargs={132                    "filepath": os.path.join(data_dir["dataset"]),133                    "split": "train",134                },135            ),136        ]137 138    def _generate_examples(self, filepath, split):139        """Yields examples."""140 141        # Empty Segment list with annotations dictionary142        # First prompt of a conversation does not contain the segment dictionary143        # We are setting it to None values144        segments_empty = [145            {146                "startIndex": 0,147                "endIndex": 0,148                "text": "",149                "annotations": [],150            }151        ]152 153        with open(filepath, encoding="utf-8") as f:154            dataset = json.load(f)155 156            for id_, data in enumerate(dataset):157                conversationId = data["conversationId"]158 159                utterances = data["utterances"]160                for utterance in utterances:161                    if "segments" not in utterance:162                        utterance["segments"] = segments_empty.copy()163 164                yield id_, {165                    "conversationId": conversationId,166                    "utterances": utterances,167                }168