Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
English
Size:
100K<n<1M
ArXiv:
License:
| # coding=utf-8 | |
| # Copyright 2020 The TensorFlow Datasets Authors and 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. | |
| # Lint as: python3 | |
| """SEMPRE dataset.""" | |
| import json | |
| import os | |
| import datasets | |
| _CITATION = r""" | |
| @article{DBLP:journals/corr/DunnSHGCC17, | |
| author = {Matthew Dunn and | |
| Levent Sagun and | |
| Mike Higgins and | |
| V. Ugur G{\"{u}}ney and | |
| Volkan Cirik and | |
| Kyunghyun Cho}, | |
| title = {SearchQA: {A} New Q{\&}A Dataset Augmented with Context from a | |
| Search Engine}, | |
| journal = {CoRR}, | |
| volume = {abs/1704.05179}, | |
| year = {2017}, | |
| url = {http://arxiv.org/abs/1704.05179}, | |
| archivePrefix = {arXiv}, | |
| eprint = {1704.05179}, | |
| timestamp = {Mon, 13 Aug 2018 16:47:09 +0200}, | |
| biburl = {https://dblp.org/rec/journals/corr/DunnSHGCC17.bib}, | |
| bibsource = {dblp computer science bibliography, https://dblp.org} | |
| } | |
| """ | |
| # pylint: disable=line-too-long | |
| _DESCRIPTION = """ | |
| We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind | |
| CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an existing article | |
| and generate a question-answer pair, but start from an existing question-answer pair, crawled from J! Archive, and augment it with text snippets retrieved by Google. | |
| Following this approach, we built SearchQA, which consists of more than 140k question-answer pairs with each pair having 49.6 snippets on average. Each question-answer-context | |
| tuple of the SearchQA comes with additional meta-data such as the snippet's URL, which we believe will be valuable resources for future research. We conduct human evaluation | |
| as well as test two baseline methods, one simple word selection and the other deep learning based, on the SearchQA. We show that there is a meaningful gap between the human | |
| and machine performances. This suggests that the proposed dataset could well serve as a benchmark for question-answering. | |
| """ | |
| _DL_URLS = { | |
| "raw_jeopardy": "https://drive.google.com/uc?export=download&id=1U7WdBpd9kJ85S7BbBhWUSiy9NnXrKdO6", | |
| "train_test_val": "https://drive.google.com/uc?export=download&id=1aHPVfC5TrlnUjehtagVZoDfq4VccgaNT", | |
| } | |
| # pylint: enable=line-too-long | |
| class SearchQaConfig(datasets.BuilderConfig): | |
| """BuilderConfig for SearchQA.""" | |
| def __init__(self, data_url, **kwargs): | |
| """BuilderConfig for SearchQA | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(SearchQaConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) | |
| self.data_url = data_url | |
| class SearchQa(datasets.GeneratorBasedBuilder): | |
| """Search QA Dataset.""" | |
| BUILDER_CONFIGS = [SearchQaConfig(name=name, description="", data_url=_DL_URLS[name]) for name in _DL_URLS.keys()] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION + "\n" + self.config.description, | |
| features=datasets.Features( | |
| { | |
| "category": datasets.Value("string"), | |
| "air_date": datasets.Value("string"), | |
| "question": datasets.Value("string"), | |
| "value": datasets.Value("string"), | |
| "answer": datasets.Value("string"), | |
| "round": datasets.Value("string"), | |
| "show_number": datasets.Value("int32"), | |
| "search_results": datasets.features.Sequence( | |
| { | |
| "urls": datasets.Value("string"), | |
| "snippets": datasets.Value("string"), | |
| "titles": datasets.Value("string"), | |
| "related_links": datasets.Value("string"), | |
| } | |
| ) | |
| # These are the features of your dataset like images, labels ... | |
| } | |
| ), | |
| homepage="https://github.com/nyu-dl/dl4ir-searchQA", | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| # TODO(jeopardy): Downloads the data and defines the splits | |
| # dl_manager is a datasets.download.DownloadManager that can be used to | |
| # download and extract URLs | |
| if self.config.name == "raw_jeopardy": | |
| filepath = dl_manager.download_and_extract(_DL_URLS["raw_jeopardy"]) | |
| sub_folders = sorted(os.listdir(os.path.join(filepath, "jeopardy"))) | |
| all_files = [] | |
| for zip_folder in sub_folders: | |
| if "lock" in zip_folder: | |
| continue | |
| zip_folder_path = os.path.join(filepath, "jeopardy", zip_folder) | |
| file_path = dl_manager.extract(zip_folder_path) | |
| zip_folder = zip_folder.split(".")[0] | |
| if os.path.isdir(os.path.join(file_path, zip_folder)): | |
| file_path = os.path.join(file_path, zip_folder) | |
| else: | |
| # in some cases the subfolder name contains sapces as 050000 - 059999 and 050000-059999 | |
| parts = zip_folder.split("-") | |
| zip_folder = parts[0] + " - " + parts[1] | |
| if os.path.isdir(os.path.join(file_path, zip_folder)): | |
| file_path = os.path.join(file_path, zip_folder) | |
| files = sorted(os.listdir(file_path)) | |
| files_paths = [os.path.join(file_path, file) for file in files if "__MACOSX" not in file] | |
| all_files.extend(files_paths) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": all_files}), | |
| ] | |
| elif self.config.name == "train_test_val": | |
| filepath = dl_manager.download_and_extract(_DL_URLS["train_test_val"]) | |
| train_path = dl_manager.extract(os.path.join(filepath, "data_json", "train.zip")) | |
| test_path = dl_manager.extract(os.path.join(filepath, "data_json", "test.zip")) | |
| val_path = dl_manager.extract(os.path.join(filepath, "data_json", "val.zip")) | |
| train_files = [os.path.join(train_path, file) for file in sorted(os.listdir(train_path))] | |
| test_files = [os.path.join(test_path, file) for file in sorted(os.listdir(test_path))] | |
| val_files = [os.path.join(val_path, file) for file in sorted(os.listdir(val_path))] | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_files}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": test_files}), | |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": val_files}), | |
| ] | |
| def _generate_examples(self, filepaths): | |
| """Yields examples.""" | |
| # TODO(searchQa): Yields (key, example) tuples from the dataset | |
| for i, filepath in enumerate(filepaths): | |
| with open(filepath, encoding="utf-8") as f: | |
| data = json.load(f) | |
| category = data["category"] | |
| air_date = data["air_date"] | |
| question = data["question"] | |
| value = data["value"] | |
| answer = data["answer"] | |
| round_ = data["round"] | |
| show_number = int(data["show_number"]) | |
| search_results = data["search_results"] | |
| urls = [result["url"] for result in search_results] | |
| snippets = [result["snippet"] for result in search_results] | |
| titles = [result["title"] for result in search_results] | |
| related_links = [ | |
| result["related_links"] if result["related_links"] else "" for result in search_results | |
| ] | |
| yield i, { | |
| "category": category, | |
| "air_date": air_date, | |
| "question": question, | |
| "value": value, | |
| "answer": answer, | |
| "round": round_, | |
| "category": category, | |
| "show_number": show_number, | |
| "search_results": { | |
| "urls": urls, | |
| "snippets": snippets, | |
| "titles": titles, | |
| "related_links": related_links, | |
| }, | |
| } | |