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"""qanta dataset.""" |
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import json |
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from typing import List, Tuple |
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import datasets |
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_CITATION = """ |
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@article{Rodriguez2019QuizbowlTC, |
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title={Quizbowl: The Case for Incremental Question Answering}, |
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author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber}, |
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journal={ArXiv}, |
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year={2019}, |
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volume={abs/1904.04792} |
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} |
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""" |
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_DESCRIPTION = """ |
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The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl. |
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""" |
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_QANTA_URL = "https://s3-us-west-2.amazonaws.com/pinafore-us-west-2/qanta-jmlr-datasets/qanta.mapped.2018.04.18.json" |
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_TRICK_URL = "https://s3-us-west-2.amazonaws.com/pinafore-us-west-2/trick-tacl-datasets/qanta.tacl-trick.json" |
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_VERSION = datasets.Version("2018.04.18") |
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_FIRST = "first" |
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_FULL = "full" |
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_SENTENCES = "sentences" |
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_RUNS = "runs" |
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_MODES = [_FULL, _FIRST, _SENTENCES, _RUNS] |
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_DEFAULT_CHAR_SKIP = 25 |
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class QantaConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Qanta.""" |
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def __init__(self, mode: str, char_skip: int, **kwargs): |
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super(QantaConfig, self).__init__(version=_VERSION, **kwargs) |
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self.mode = mode |
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self.char_skip = char_skip |
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def create_char_runs(text: str, char_skip: int) -> List[Tuple[str, int]]: |
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""" |
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Returns runs of the question based on skipping char_skip characters at a time. Also returns the indices used |
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q: name this first united states president. |
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runs with char_skip=10: |
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['name this ', |
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'name this first unit', |
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'name this first united state p', |
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'name this first united state president.'] |
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:param char_skip: Number of characters to skip each time |
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""" |
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char_indices = list(range(char_skip, len(text) + char_skip, char_skip)) |
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return [(text[:idx], idx) for idx in char_indices] |
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def with_default(key, lookup, default): |
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if key in lookup: |
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value = lookup[key] |
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if value is None: |
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return default |
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else: |
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return value |
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else: |
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return default |
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def question_to_examples(question, mode: str, char_skip: int): |
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features = { |
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"qanta_id": question["qanta_id"], |
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"proto_id": with_default("proto_id", question, ""), |
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"qdb_id": with_default("qdb_id", question, -1), |
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"page": question["page"], |
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"answer": question["page"], |
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"raw_answer": question["answer"], |
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"dataset": with_default("dataset", question, ""), |
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"full_question": question["text"], |
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"first_sentence": question["first_sentence"], |
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"tokenizations": question["tokenizations"], |
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"fold": question["fold"], |
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"gameplay": question["gameplay"], |
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"category": with_default("category", question, ""), |
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"subcategory": with_default("subcategory", question, ""), |
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"tournament": question["tournament"], |
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"difficulty": with_default("difficulty", question, ""), |
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"year": question["year"], |
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"char_idx": -1, |
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"sentence_idx": -1, |
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} |
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if mode == _FULL: |
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yield { |
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"text": question["text"], |
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"id": str(question["qanta_id"]) + "-full", |
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**features, |
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} |
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elif mode == _FIRST: |
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yield { |
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"text": question["first_sentence"], |
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"id": str(question["qanta_id"]) + "-first", |
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**features, |
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} |
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elif mode == _RUNS: |
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text = question["text"] |
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for text_run, char_idx in create_char_runs(text, char_skip): |
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yield { |
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"text": text_run, |
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"char_idx": char_idx, |
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"id": str(question["qanta_id"]) + "-char-" + str(char_idx), |
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**features, |
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} |
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elif mode == _SENTENCES: |
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for sentence_idx, (start, end) in enumerate(question["tokenizations"]): |
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sentence = question["text"][start:end] |
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yield { |
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"text": sentence, |
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"sentence_idx": sentence_idx, |
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"id": str(question["qanta_id"]) + "-sentence-" + str(sentence_idx), |
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**features, |
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} |
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else: |
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raise ValueError(f"Invalid mode: {mode}") |
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_FEATURES = { |
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"id": datasets.Value("string"), |
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"qanta_id": datasets.Value("int32"), |
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"proto_id": datasets.Value("string"), |
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"qdb_id": datasets.Value("int32"), |
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"dataset": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"full_question": datasets.Value("string"), |
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"first_sentence": datasets.Value("string"), |
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"char_idx": datasets.Value("int32"), |
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"sentence_idx": datasets.Value("int32"), |
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"tokenizations": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("int32"), length=2)), |
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"answer": datasets.Value("string"), |
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"page": datasets.Value("string"), |
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"raw_answer": datasets.Value("string"), |
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"fold": datasets.Value("string"), |
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"gameplay": datasets.Value("bool"), |
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"category": datasets.Value("string"), |
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"subcategory": datasets.Value("string"), |
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"tournament": datasets.Value("string"), |
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"difficulty": datasets.Value("string"), |
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"year": datasets.Value("int32"), |
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} |
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class Qanta(datasets.GeneratorBasedBuilder): |
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"""The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl.""" |
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VERSION = _VERSION |
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BUILDER_CONFIGS = [ |
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QantaConfig( |
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name=f"mode={mode},char_skip={_DEFAULT_CHAR_SKIP}", |
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description=f"Question format: {mode}, char_skip: {_DEFAULT_CHAR_SKIP}", |
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mode=mode, |
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char_skip=_DEFAULT_CHAR_SKIP, |
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) |
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for mode in _MODES |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features(_FEATURES), |
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supervised_keys=None, |
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homepage="http://www.qanta.org/", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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qanta_path = dl_manager.download_and_extract(_QANTA_URL) |
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trick_path = dl_manager.download_and_extract(_TRICK_URL) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split("guesstrain"), |
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gen_kwargs={ |
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"qanta_filepath": qanta_path, |
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"trick_filepath": trick_path, |
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"fold": "guesstrain", |
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"mode": self.config.mode, |
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"char_skip": self.config.char_skip, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split("buzztrain"), |
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gen_kwargs={ |
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"qanta_filepath": qanta_path, |
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"trick_filepath": trick_path, |
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"fold": "buzztrain", |
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"mode": self.config.mode, |
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"char_skip": self.config.char_skip, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split("guessdev"), |
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gen_kwargs={ |
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"qanta_filepath": qanta_path, |
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"trick_filepath": trick_path, |
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"fold": "guessdev", |
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"mode": self.config.mode, |
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"char_skip": self.config.char_skip, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split("buzzdev"), |
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gen_kwargs={ |
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"qanta_filepath": qanta_path, |
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"trick_filepath": trick_path, |
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"fold": "buzzdev", |
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"mode": self.config.mode, |
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"char_skip": self.config.char_skip, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split("guesstest"), |
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gen_kwargs={ |
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"qanta_filepath": qanta_path, |
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"trick_filepath": trick_path, |
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"fold": "guesstest", |
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"mode": self.config.mode, |
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"char_skip": self.config.char_skip, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split("buzztest"), |
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gen_kwargs={ |
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"qanta_filepath": qanta_path, |
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"trick_filepath": trick_path, |
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"fold": "buzztest", |
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"mode": self.config.mode, |
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"char_skip": self.config.char_skip, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split("adversarial"), |
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gen_kwargs={ |
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"qanta_filepath": qanta_path, |
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"trick_filepath": trick_path, |
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"fold": "adversarial", |
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"mode": self.config.mode, |
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"char_skip": self.config.char_skip, |
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}, |
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), |
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] |
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def _generate_examples( |
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self, |
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qanta_filepath: str, |
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trick_filepath: str, |
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fold: str, |
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mode: str, |
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char_skip: int, |
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): |
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"""Yields examples.""" |
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if mode not in _MODES: |
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raise ValueError(f"Invalid mode: {mode}") |
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if fold == "adversarial": |
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path = trick_filepath |
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else: |
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path = qanta_filepath |
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with open(path, encoding="utf-8") as f: |
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questions = json.load(f)["questions"] |
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for q in questions: |
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if q["page"] is not None and q["fold"] == fold: |
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for example in question_to_examples(q, mode, char_skip): |
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yield example["id"], example |
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