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