| """LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning""" |
|
|
| import re |
| import datasets |
|
|
| logger = datasets.logging.get_logger(__name__) |
|
|
|
|
| _HOMEPAGE = "https://github.com/lgw863/LogiQA-dataset" |
|
|
| _DESCRIPTION = """\ |
| LogiQA is constructed from the logical comprehension problems from \ |
| publically available questions of the National Civil Servants Examination \ |
| of China, which are designed to test the civil servant candidates’ critical \ |
| thinking and problem solving. This dataset includes the English versions only; \ |
| the Chinese versions are available via the homepage/original source.""" |
|
|
| _CITATION = """\ |
| @article{liu2020logiqa, |
| title={Logiqa: A challenge dataset for machine reading comprehension with logical reasoning}, |
| author={Liu, Jian and Cui, Leyang and Liu, Hanmeng and Huang, Dandan and Wang, Yile and Zhang, Yue}, |
| journal={arXiv preprint arXiv:2007.08124}, |
| year={2020} |
| } |
| """ |
|
|
| _URLS = { |
| "en_train": "https://raw.githubusercontent.com/lgw863/LogiQA-dataset/master/Train.txt", |
| "en_test": "https://raw.githubusercontent.com/lgw863/LogiQA-dataset/master/Test.txt", |
| "en_eval": "https://raw.githubusercontent.com/lgw863/LogiQA-dataset/master/Eval.txt", |
| } |
|
|
| def _process_answer(answer): |
| if not any(answer.startswith(x) for x in "ABCD"): |
| return answer |
| else: |
| return answer[3:] |
|
|
| def _process_sentences(text): |
| text = text.replace("\n", "") |
| sents = text.split(".") |
| text = "" |
| for sent in sents: |
| if len(sent) == 0: |
| continue |
| if len(text) == 0: |
| text += sent |
| elif sent[0].isnumeric(): |
| text += "."+sent |
| else: |
| text += ". "+sent |
| text = text.replace(" ", " ") |
| text = text.replace("\\'", "'") |
| while text.endswith(" "): |
| text = text[:-1] |
| if re.match('^[A-Z][\w\s]+[?.!]$', text) is None: |
| text += "." |
| text = text.replace("?.", "?") |
| text = text.replace("!.", "!") |
| text = text.replace("..", ".") |
| return text |
|
|
| class LogiQA(datasets.GeneratorBasedBuilder): |
| def _info(self): |
| features = datasets.Features( |
| { |
| "context": datasets.Value("string"), |
| "query": datasets.Value("string"), |
| "options": datasets.features.Sequence(datasets.Value("string")), |
| "correct_option": datasets.Value("int32") |
| } |
| ) |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=features, |
| homepage=_HOMEPAGE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| downloaded_files = dl_manager.download_and_extract(_URLS) |
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["en_train"]}), |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["en_eval"]}), |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["en_test"]}), |
| ] |
|
|
| def _generate_examples(self, filepath): |
| logger.info("generating examples from = %s", filepath) |
| with open(filepath, encoding="utf-8") as f: |
| logiqa = f.readlines() |
| logiqa = [_process_sentences(s) for s in logiqa] |
|
|
| for key in range(int(len(logiqa)/8)): |
| row = 8*key |
| correct_answer = logiqa[row+1].replace(".","") |
| context = logiqa[row+2] |
| query = logiqa[row+3] |
| answers = logiqa[row+4:row+8] |
|
|
| yield key, { |
| "context": context, |
| "query": query, |
| "options": [_process_answer(answers[i]) for i in range(4)], |
| "correct_option": "abcd".index(correct_answer) |
| } |
|
|
