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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
+