Datasets:
Tasks:
Question Answering
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
License:
"""qanta dataset.""" | |
from __future__ import absolute_import, division, print_function | |
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 | |