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qa_srl / qa_srl.py

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	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""Dataset loading script for loading the Large-Scale-QASRL (FitzGeralds et. al., ACL 2018) training set, along with the QASRL-GS evaluation dataset (Roit et. al., ACL 2020)."""


	import datasets
	from pathlib import Path
	import gzip
	import json


	_CITATION = """\
	@inproceedings{fitzgerald2018large,
	title={Large-Scale QA-SRL Parsing},
	author={FitzGerald, Nicholas and Michael, Julian and He, Luheng and Zettlemoyer, Luke},
	booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
	pages={2051--2060},
	year={2018}
	}
	@inproceedings{roit2020controlled,
	title={Controlled Crowdsourcing for High-Quality QA-SRL Annotation},
	author={Roit, Paul and Klein, Ayal and Stepanov, Daniela and Mamou, Jonathan and Michael, Julian and Stanovsky, Gabriel and Zettlemoyer, Luke and Dagan, Ido},
	booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
	pages={7008--7013},
	year={2020}
	}
	"""


	_DESCRIPTION = """\
	The dataset contains question-answer pairs to model verbal predicate-argument structure.
	The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence.
	This dataset loads the train split from "QASRL Bank", a.k.a "QASRL-v2" or "QASRL-LS" (Large Scale),
	which was constructed via crowdsourcing and presented at (FitzGeralds et. al., ACL 2018),
	and the dev and test splits from QASRL-GS (Gold Standard), introduced in (Roit et. al., ACL 2020).
	"""

	_HOMEPAGE = "https://qasrl.org"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = ""

	SpanFeatureType = datasets.Sequence(datasets.Value("int32"), length=2)

	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class QaSrl(datasets.GeneratorBasedBuilder):
	"""QA-SRL: Question-Answer Driven Semantic Role Labeling corpus"""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="plain_text", version=VERSION, description=""
	),
	]

	DEFAULT_CONFIG_NAME = (
	"plain_text" # It's not mandatory to have a default configuration. Just use one if it make sense.
	)

	def _info(self):
	features = datasets.Features(
	{
	"sentence": datasets.Value("string"),
	"sent_id": datasets.Value("string"),
	"predicate_idx": datasets.Value("int32"),
	"predicate": datasets.Value("string"),
	"is_verbal": datasets.Value("bool"),
	"verb_form": datasets.Value("string"),
	"question": datasets.Sequence(datasets.Value("string")),
	"answers": datasets.Sequence(datasets.Value("string")),
	"answer_ranges": datasets.Sequence(SpanFeatureType)
	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager: datasets.utils.download_manager.DownloadManager):
	"""Returns SplitGenerators."""

	# iterate the tar file of the corpus

	# Older version of the corpus (has some format errors):
	# corpus_base_path = Path(dl_manager.download_and_extract(_URLs["qasrl_v2.0"]))
	# corpus_orig = corpus_base_path / "qasrl-v2" / "orig"

	self.qasrl2018 = datasets.load_dataset("biu-nlp/qa_srl2018")
	self.qasrl2020 = datasets.load_dataset("biu-nlp/qa_srl2020")


	# TODO add optional kwarg for genre (wikinews)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"dataset": self.qasrl2018["train"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"dataset": self.qasrl2020["validation"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"dataset": self.qasrl2020["test"],
	},
	),
	]

	def _generate_examples(self, dataset):

	""" Yields examples from a '.jsonl.gz' file ."""
	for idx, instance in enumerate(dataset):
	yield idx, instance