Datasets:
Sub-tasks:
dialogue-modeling
Languages:
English
Size:
10K<n<100K
Tags:
conversational-curiosity
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. | |
| """Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity""" | |
| import json | |
| import os | |
| import datasets | |
| _CITATION = """\ | |
| @inproceedings{rodriguez2020curiosity, | |
| title = {Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity}, | |
| author = {Pedro Rodriguez and Paul Crook and Seungwhan Moon and Zhiguang Wang}, | |
| year = 2020, | |
| booktitle = {Empirical Methods in Natural Language Processing} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| This dataset contains 14K dialogs (181K utterances) where users and assistants converse about geographic topics like | |
| geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog | |
| acts, grounding to Wikipedia, and user reactions to messages. | |
| """ | |
| _HOMEPAGE = "https://www.pedro.ai/curiosity" | |
| _LICENSE = "https://github.com/facebookresearch/curiosity/blob/master/LICENSE" | |
| _URL = "https://obj.umiacs.umd.edu/curiosity/" | |
| _URLs = { | |
| "train": _URL + "curiosity_dialogs.train.json", | |
| "val": _URL + "curiosity_dialogs.val.json", | |
| "test": _URL + "curiosity_dialogs.test.json", | |
| "test_zero": _URL + "curiosity_dialogs.test_zero.json", | |
| } | |
| class CuriosityDialogsConfig(datasets.BuilderConfig): | |
| """BuilderConfig for Curiosity Dialogs dataset""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for Curiosity Dialogs dataset. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(CuriosityDialogsConfig, self).__init__(**kwargs) | |
| class CuriosityDialogs(datasets.GeneratorBasedBuilder): | |
| """Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity""" | |
| VERSION = datasets.Version("1.1.0") | |
| BUILDER_CONFIGS = [ | |
| CuriosityDialogsConfig( | |
| name="curiosity_dialogs", | |
| version=datasets.Version("1.1.0"), | |
| description="Curiosity Dialog: A Dataset for Conversational Curiosity", | |
| ), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "messages": datasets.Sequence( | |
| { | |
| "message": datasets.Value("string"), | |
| "liked": datasets.ClassLabel(names=["False", "True"]), | |
| "sender": datasets.ClassLabel(names=["user", "assistant"]), | |
| "facts": datasets.Sequence( | |
| { | |
| "fid": datasets.Value("int32"), | |
| "used": datasets.ClassLabel(names=["False", "True"]), | |
| "source": datasets.ClassLabel(names=["section", "known", "random"]), | |
| } | |
| ), | |
| "message_id": datasets.Value("string"), | |
| "dialog_acts": datasets.Sequence(datasets.Value("string")), | |
| } | |
| ), | |
| "known_entities": datasets.Sequence(datasets.Value("string")), | |
| "focus_entity": datasets.Value("string"), | |
| "dialog_id": datasets.Value("int32"), | |
| "inferred_steps": datasets.ClassLabel(names=["False", "True"]), | |
| "created_time": datasets.Value("int64"), | |
| "aspects": datasets.Sequence(datasets.Value("string")), | |
| "first_aspect": datasets.Value("string"), | |
| "second_aspect": datasets.Value("string"), | |
| "shuffle_facts": datasets.ClassLabel(names=["False", "True"]), | |
| "related_entities": datasets.Sequence(datasets.Value("string")), | |
| "tag": datasets.Value("string"), | |
| "user_id": datasets.Value("int32"), | |
| "assistant_id": datasets.Value("int32"), | |
| "is_annotated": datasets.ClassLabel(names=["False", "True"]), | |
| "user_dialog_rating": datasets.Value("int32"), | |
| "user_other_agent_rating": datasets.Value("int32"), | |
| "assistant_dialog_rating": datasets.Value("int32"), | |
| "assistant_other_agent_rating": datasets.Value("int32"), | |
| "reported": datasets.ClassLabel(names=["False", "True"]), | |
| "annotated": datasets.ClassLabel(names=["False", "True"]), | |
| } | |
| ), | |
| 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) | |
| return [ | |
| datasets.SplitGenerator( | |
| name="train", | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir["train"]), | |
| "split": "train", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name="val", | |
| gen_kwargs={"filepath": os.path.join(data_dir["val"]), "split": "val"}, | |
| ), | |
| datasets.SplitGenerator( | |
| name="test", | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir["test"]), | |
| "split": "test_zero", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name="test_zero", | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir["test_zero"]), | |
| "split": "test_zero", | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath, split): | |
| """Yields examples.""" | |
| # Bool entries are converted to string entries because of PyArrow error | |
| with open(filepath, encoding="utf-8") as f: | |
| dataset = json.load(f) | |
| dialogs = dataset["dialogs"] | |
| for id_, data in enumerate(dialogs): | |
| messages = data["messages"] | |
| for message in messages: | |
| message["liked"] = str(message["liked"]) | |
| facts = message["facts"] | |
| for fact in facts: | |
| fact["used"] = str(fact["used"]) | |
| known_entities = data["known_entities"] | |
| focus_entity = data["focus_entity"] | |
| dialog_id = data["dialog_id"] | |
| inferred_steps = str(data["inferred_steps"]) | |
| created_time = data["created_time"] | |
| aspects = data["aspects"] | |
| first_aspect = data["first_aspect"] | |
| second_aspect = data["second_aspect"] | |
| shuffle_facts = str(data["shuffle_facts"]) | |
| related_entities = data["related_entities"] | |
| tag = data["tag"] | |
| user_id = data["user_id"] | |
| assistant_id = data["assistant_id"] | |
| is_annotated = str(data["is_annotated"]) | |
| user_dialog_rating = data["user_dialog_rating"] | |
| user_other_agent_rating = data["user_other_agent_rating"] | |
| assistant_dialog_rating = data["assistant_dialog_rating"] | |
| assistant_other_agent_rating = data["assistant_other_agent_rating"] | |
| reported = str(data["reported"]) | |
| annotated = str(data["annotated"]) | |
| yield id_, { | |
| "messages": messages, | |
| "known_entities": known_entities, | |
| "focus_entity": focus_entity, | |
| "dialog_id": dialog_id, | |
| "inferred_steps": inferred_steps, | |
| "created_time": created_time, | |
| "aspects": aspects, | |
| "first_aspect": first_aspect, | |
| "second_aspect": second_aspect, | |
| "shuffle_facts": shuffle_facts, | |
| "related_entities": related_entities, | |
| "tag": tag, | |
| "user_id": user_id, | |
| "assistant_id": assistant_id, | |
| "is_annotated": is_annotated, | |
| "user_dialog_rating": user_dialog_rating, | |
| "user_other_agent_rating": user_other_agent_rating, | |
| "assistant_dialog_rating": assistant_dialog_rating, | |
| "assistant_other_agent_rating": assistant_other_agent_rating, | |
| "reported": reported, | |
| "annotated": annotated, | |
| } | |