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| """Inquisitive Question Generation for High Level Text Comprehension""" |
|
|
|
|
| import itertools |
|
|
| import datasets |
|
|
|
|
| _CITATION = """\ |
| @InProceedings{ko2020inquisitive, |
| author = {Ko, Wei-Jen and Chen, Te-Yuan and Huang, Yiyan and Durrett, Greg and Li, Junyi Jessy}, |
| title = {Inquisitive Question Generation for High Level Text Comprehension}, |
| booktitle = {Proceedings of EMNLP}, |
| year = {2020}, |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| A dataset of about 20k questions that are elicited from readers as they naturally read through a document sentence by sentence. \ |
| Compared to existing datasets, INQUISITIVE questions target more towards high-level (semantic and discourse) comprehension of text. \ |
| Because these questions are generated while the readers are processing the information, the questions directly communicate gaps between \ |
| the reader’s and writer’s knowledge about the events described in the text, and are not necessarily answered in the document itself. \ |
| This type of question reflects a real-world scenario: if one has questions during reading, some of them are answered by the text later on, \ |
| the rest are not, but any of them would help further the reader’s understanding at the particular point when they asked it. \ |
| This resource could enable question generation models to simulate human-like curiosity and cognitive processing, which may open up a new realm of applications. |
| """ |
|
|
| _ARTICLES_URL = "https://github.com/wjko2/INQUISITIVE/raw/master/articles.tgz" |
| _QUESTIONS_URL = "https://github.com/wjko2/INQUISITIVE/raw/master/questions.txt" |
|
|
| ALL_ARTICLE_IDS = list(range(1, 1501)) |
| DEV_ARTICLE_IDS = list(itertools.chain(range(1, 101), range(1051, 1101))) |
| TEST_ARTICLE_IDS = list(itertools.chain(range(101, 151), range(501, 551), range(1101, 1151))) |
| DEV_AND_TEST_IDS = DEV_ARTICLE_IDS + TEST_ARTICLE_IDS |
| TRAIN_ARTICLE_IDS = [id_ for id_ in ALL_ARTICLE_IDS if id_ not in DEV_AND_TEST_IDS] |
|
|
|
|
| class InquisitiveQgConfig(datasets.BuilderConfig): |
| """BuilderConfig for INQUISITIVE.""" |
|
|
| def __init__(self, **kwrags): |
| """BuilderConfig for INQUISITIVE. |
| |
| Args: |
| **kwargs: keyword arguments forwarded to super. |
| """ |
| super(InquisitiveQgConfig, self).__init__(**kwrags) |
|
|
|
|
| class InquisitiveQg(datasets.GeneratorBasedBuilder): |
| """Inquisitive Question Generation for High Level Text Comprehension""" |
|
|
| VERSION = datasets.Version("1.0.0") |
| BUILDER_CONFIGS = [ |
| InquisitiveQgConfig(name="plain_text", version=datasets.Version("1.0.0", ""), description="plain_text"), |
| ] |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features( |
| { |
| "id": datasets.Value("int32"), |
| "article_id": datasets.Value("int32"), |
| "article": datasets.Value("string"), |
| "sentence_id": datasets.Value("int32"), |
| "sentence": datasets.Value("string"), |
| "span": datasets.Value("string"), |
| "question": datasets.Value("string"), |
| "span_start_position": datasets.Value("int32"), |
| "span_end_position": datasets.Value("int32"), |
| } |
| ), |
| supervised_keys=None, |
| homepage="https://github.com/wjko2/INQUISITIVE", |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| questions_file = dl_manager.download(_QUESTIONS_URL) |
| archive = dl_manager.download(_ARTICLES_URL) |
| articles_dir = "article" |
|
|
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "articles_dir": articles_dir, |
| "questions_file": questions_file, |
| "article_ids": TRAIN_ARTICLE_IDS, |
| "files": dl_manager.iter_archive(archive), |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "articles_dir": articles_dir, |
| "questions_file": questions_file, |
| "article_ids": DEV_ARTICLE_IDS, |
| "files": dl_manager.iter_archive(archive), |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "articles_dir": articles_dir, |
| "questions_file": questions_file, |
| "article_ids": TEST_ARTICLE_IDS, |
| "files": dl_manager.iter_archive(archive), |
| }, |
| ), |
| ] |
|
|
| def _generate_examples(self, articles_dir, questions_file, article_ids, files): |
| articles = {} |
| for path, f in files: |
| articles[path] = f.read().decode("utf-8") |
| with open(questions_file, encoding="utf-8") as f: |
| questions_counter = 0 |
| rows = f.readlines() |
| for i, row in enumerate(rows): |
| if i == 0: |
| continue |
| row = row.strip() |
| cols = row.split("\t") |
|
|
| article_id = int(cols[0]) |
| if article_id not in article_ids: |
| continue |
|
|
| fname = str(article_id).rjust(4, "0") + ".txt" |
| article_path = articles_dir + "/" + fname |
| article = articles[article_path] |
|
|
| id_ = str(questions_counter) |
| example = { |
| "article_id": article_id, |
| "sentence_id": int(cols[1]), |
| "sentence": cols[2], |
| "span": cols[3], |
| "question": cols[4], |
| "span_start_position": cols[5], |
| "span_end_position": cols[6], |
| "id": id_, |
| "article": article, |
| } |
| yield id_, example |
| questions_counter += 1 |
|
|