adding 'is_verbal' and 'verb_form' fixed columns, to align with qanom and qa_srl2020

qa_srl2018.py

@@ -0,0 +1,186 @@
+# 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.
+"""TODO: Add a description here."""
+
+
+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}
+}
+"""
+
+
+_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, a.k.a "QASRL Bank", "QASRL-v2" or "QASRL-LS" (Large Scale), was constructed via crowdsourcing.
+"""
+
+_HOMEPAGE = "https://qasrl.org"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+
+_URLs = {
+    "qasrl_v2.0": "http://qasrl.org/data/qasrl-v2.tar",
+    "qasrl_v2.1": "https://qasrl.org/data/qasrl-v2_1.tar"
+}
+
+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 QaSrl2018(datasets.GeneratorBasedBuilder):
+    """QA-SRL2018: Large-Scale Question-Answer Driven Semantic Role Labeling corpus"""
+
+    VERSION = datasets.Version("1.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain_text", version=VERSION, description="This provides WIKIPEDIA dataset for qa_srl corpus"
+        ),
+    ]
+
+    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"        
+        
+        corpus_base_path = Path(dl_manager.download_and_extract(_URLs["qasrl_v2.1"]))
+        corpus_orig = corpus_base_path / "qasrl-v2_1" / "orig"
+        
+        
+        # TODO add optional kwarg for genre (wikinews) 
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": corpus_orig / "train.jsonl.gz",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": corpus_orig / "dev.jsonl.gz",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": corpus_orig / "test.jsonl.gz",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+
+        """ Yields examples from a '.jsonl.gz' file ."""
+
+        with gzip.open(filepath, "rt") as f:
+            qa_counter = 0
+            for line in f:
+                sent_obj = json.loads(line.strip())
+                tokens = sent_obj['sentenceTokens']
+                sentence = ' '.join(tokens)
+                for predicate_idx, verb_obj in sent_obj['verbEntries'].items():
+                    verb_forms = verb_obj['verbInflectedForms']
+                    predicate = tokens[int(predicate_idx)]
+                    for question_obj in verb_obj['questionLabels'].values():
+                        question_slots = question_obj['questionSlots']
+                        verb_form = question_slots['verb'] 
+                        verb_surface = verb_forms[verb_form.split(" ")[-1]] # if verb_form in verb_forms else verb_forms['stem']
+                        question_slots_in_order = [
+                            question_slots["wh"],
+                            question_slots["aux"],
+                            question_slots["subj"],                            
+                            verb_surface,
+                            question_slots["obj"],
+                            question_slots["prep"],
+                            question_slots["obj2"],
+                            '?'
+                        ] 
+                        # retrieve answers
+                        answer_spans = []
+                        for ans in question_obj['answerJudgments']:
+                            if ans['isValid']: 
+                                answer_spans.extend(ans['spans']) 
+                        answer_spans = list(set(tuple(a) for a in answer_spans))
+                        # answer_spans = list(set(answer_spans))
+                        answer_strs = [' '.join([tokens[i] for i in range(*span)])
+                                   for span in answer_spans]
+                        
+                        yield qa_counter, {
+                            "sentence": sentence,
+                            "sent_id": sent_obj['sentenceId'],
+                            "predicate_idx": predicate_idx,
+                            "predicate": predicate,
+                            "is_verbal": True,
+                            "verb_form": predicate,
+                            "question": question_slots_in_order,
+                            "answers": answer_strs,
+                            "answer_ranges": answer_spans
+                        }
+                        qa_counter += 1