Datasets:
Tasks:
Question Answering
Modalities:
Text
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
1K - 10K
License:
| # 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. | |
| """MCTest: Machine comprehension test: http://research.microsoft.com/mct""" | |
| import os | |
| import datasets | |
| _CITATION = """\ | |
| @inproceedings{richardson-etal-2013-mctest, | |
| title = "{MCT}est: A Challenge Dataset for the Open-Domain Machine Comprehension of Text", | |
| author = "Richardson, Matthew and | |
| Burges, Christopher J.C. and | |
| Renshaw, Erin", | |
| booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", | |
| month = oct, | |
| year = "2013", | |
| address = "Seattle, Washington, USA", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://www.aclweb.org/anthology/D13-1020", | |
| pages = "193--203", | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| MCTest requires machines to answer multiple-choice reading comprehension questions about fictional stories, directly tackling the high-level goal of open-domain machine comprehension. | |
| """ | |
| _HOMEPAGE = "https://www.aclweb.org/anthology/D13-1020/" | |
| _DATA_URL = "http://parl.ai/downloads/mctest/mctest.tar.gz" | |
| class MCTest(datasets.GeneratorBasedBuilder): | |
| """MCTest: Machine comprehension test: http://research.microsoft.com/mct""" | |
| VERSION = datasets.Version("1.0.0") | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig( | |
| name="mc500", | |
| version=VERSION, | |
| description="MC 500", | |
| ), | |
| datasets.BuilderConfig( | |
| name="mc160", | |
| version=VERSION, | |
| description="MC 160", | |
| ), | |
| ] | |
| DEFAULT_CONFIG_NAME = "mc500" | |
| def _info(self): | |
| if self.config.name == "mc500": | |
| features = datasets.Features( | |
| { | |
| "idx": dict( | |
| {"story": datasets.Value("string"), | |
| "question": datasets.Value("int32") | |
| } | |
| ), | |
| "question": datasets.Value("string"), | |
| "story": datasets.Value("string"), | |
| "properties": dict( | |
| { | |
| "author": datasets.Value("string"), | |
| "work_time": datasets.Value("int32"), | |
| "quality_score": datasets.Value("int32"), | |
| "creativity_words": datasets.Sequence(datasets.Value("string")), | |
| } | |
| ), | |
| "answer_options": dict( | |
| { | |
| "A": datasets.Value("string"), | |
| "B": datasets.Value("string"), | |
| "C": datasets.Value("string"), | |
| "D": datasets.Value("string") | |
| } | |
| ), | |
| "answer": datasets.Value("string"), | |
| "question_is_multiple": datasets.Value("bool") | |
| } | |
| ) | |
| else: | |
| features = datasets.Features( | |
| { | |
| "idx": dict( | |
| {"story": datasets.Value("string"), | |
| "question": datasets.Value("int32") | |
| } | |
| ), | |
| "question": datasets.Value("string"), | |
| "story": datasets.Value("string"), | |
| "properties": dict( | |
| { | |
| "author": datasets.Value("string"), | |
| "work_time": datasets.Value("int32"), | |
| } | |
| ), | |
| "answer_options": dict( | |
| { | |
| "A": datasets.Value("string"), | |
| "B": datasets.Value("string"), | |
| "C": datasets.Value("string"), | |
| "D": datasets.Value("string") | |
| } | |
| ), | |
| "answer": datasets.Value("string"), | |
| "question_is_multiple": datasets.Value("bool") | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_dir = os.path.join(dl_manager.download_and_extract(_DATA_URL), 'mctest') | |
| paths = {} | |
| for phase in ["train", "dev", "test"]: | |
| paths[phase] = { | |
| "data": os.path.join(data_dir, "MCTest", f"{self.config.name}.{phase}.tsv"), | |
| "answer": os.path.join(data_dir, "MCTest", f"{self.config.name}.{phase}.ans") | |
| } | |
| paths["test"]["answer"] = os.path.join(data_dir, "MCTestAnswers", f"{self.config.name}.test.ans") | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={"filepath": paths["train"]}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={"filepath": paths["dev"]}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={"filepath": paths["test"]}, | |
| ), | |
| ] | |
| def _get_properties(self, property_str): | |
| """ | |
| properties is a semicolon-delimited list of property:value pairs, including | |
| Author (anonymized author id, consistent across all files) | |
| Work Time(s): Seconds between author accepting and submitting the task | |
| Qual. score: The author's grammar qualification test score (% correct) | |
| Creativity Words: Words the author was given to encourage creativity | |
| (there are no creativity words or qual score for mc160, see paper) | |
| :param property_str: | |
| :return: | |
| """ | |
| properties = property_str.split(';') | |
| property_data = { | |
| "author": properties[0].split(':')[-1].strip(), | |
| "work_time": int(properties[1].split(':')[-1].strip()) | |
| } | |
| if self.config.name == "mc500": | |
| property_data.update( | |
| { | |
| "quality_score": int(properties[2].split(':')[-1].strip()), | |
| "creativity_words": properties[3].split(':')[-1].strip().split(',') | |
| } | |
| ) | |
| return property_data | |
| def _generate_examples(self, filepath): | |
| tab_char = '\t' | |
| data_path = filepath['data'] | |
| ans_path = filepath['answer'] | |
| data_lines = open(data_path, encoding="utf-8").read().split('\n')[:-1] | |
| answer_lines = open(ans_path, encoding="utf-8").read().split('\n')[:-1] | |
| for data_line, answer_line in zip(data_lines, answer_lines): | |
| data_line_split = data_line.split(tab_char) | |
| story_id = data_line_split[0] | |
| properties = self._get_properties(data_line_split[1]) | |
| story = data_line_split[2] | |
| answers = answer_line.split('\t') | |
| data_line_split = data_line_split[3:] | |
| for i in range(4): | |
| answer = answers[i] | |
| index = i*5 | |
| multiple, question_text = [x.strip() for x in data_line_split[index].strip().split(':')] | |
| question_is_multiple = True if multiple == "multiple" else False | |
| answer_options = {x: y for x, y in zip(["A", "B", "C", "D"], data_line_split[index+1:index+5])} | |
| yield f"{story_id}-{i}", { | |
| "idx": | |
| {"story": story_id, | |
| "question": i | |
| }, | |
| "question": question_text, | |
| "story": story, | |
| "properties": properties, | |
| "answer_options": answer_options, | |
| "answer": answer, | |
| "question_is_multiple": question_is_multiple | |
| } | |