First version of qanom datasets script

qanom.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.
+"""A Dataset loading script for the QANom dataset (klein et. al., COLING 2000)."""
+
+
+import datasets
+from pathlib import Path
+import pandas as pd
+
+
+_CITATION = """\
+@inproceedings{klein2020qanom,
+  title={QANom: Question-Answer driven SRL for Nominalizations},
+  author={Klein, Ayal and Mamou, Jonathan and Pyatkin, Valentina and Stepanov, Daniela and He, Hangfeng and Roth, Dan and Zettlemoyer, Luke and Dagan, Ido},
+  booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
+  pages={3069--3083},
+  year={2020}
+}
+"""
+
+
+_DESCRIPTION = """\
+The dataset contains question-answer pairs to model predicate-argument structure of deverbal nominalizations. 
+The questions start with wh-words (Who, What, Where, What, etc.) and contain a the verbal form of a nominalization from the sentence; 
+the answers are phrases in the sentence. 
+See the paper for details: QANom: Question-Answer driven SRL for Nominalizations (Klein et. al., COLING 2020)
+For previewing the QANom data along with the verbal annotations of QASRL, check out "https://browse.qasrl.org/".  
+This dataset was annotated by selected workers from Amazon Mechanical Turk.
+"""
+
+_HOMEPAGE = "https://github.com/kleinay/QANom"
+
+_LICENSE = """MIT License
+
+Copyright (c) 2020 Ayal Klein (kleinay)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE."""
+
+
+_URLs = {
+    "qanom_zip":  "https://github.com/kleinay/QANom/raw/master/qanom_dataset.zip"
+}
+
+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 Qanom(datasets.GeneratorBasedBuilder):
+    """QANom: Question-Answer driven SRL for Nominalizations corpus.
+    Notice: This datasets genrally follows the format of `qa_srl` and `kleinay\qa_srl2018` datasets. 
+    However, it extends Features to include "is_verbal" and "verb_form" fields (required for nominalizations).
+    In addition, and most critically, unlike these verbal qasrl datasets, in the qanom datset some examples
+    are for canidate nominalization which are judged to be non-predicates ("is_verbal"==False) or predicates with no QAs.
+    In these cases, the qa fields (question, answers, answer_ranges) would be empty lists. """
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain_text", version=VERSION, description="This provides the QANom dataset"
+        ),
+    ]
+
+    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 _prepare_wiktionary_verb_inflections(self, dl_manager):
+        wiktionary_url = "https://raw.githubusercontent.com/nafitzgerald/nrl-qasrl/master/data/wiktionary/en_verb_inflections.txt"
+        wiktionary_path = dl_manager.download(wiktionary_url)
+        verb_map = {}
+        with open(wiktionary_path, 'r', encoding="utf-8") as f:
+            for l in f.readlines():
+                inflections = l.strip().split('\t')
+                stem, presentsingular3rd, presentparticiple, past, pastparticiple = inflections
+                for inf in inflections:
+                    verb_map[inf] = {"Stem" : stem, "PresentSingular3rd" : presentsingular3rd, "PresentParticiple":presentparticiple, "Past":past, "PastParticiple":pastparticiple}
+        self.verb_inflections = verb_map 
+                
+    def _split_generators(self, dl_manager: datasets.utils.download_manager.DownloadManager):
+        """Returns SplitGenerators."""            
+        
+        # prepare wiktionary for verb inflections inside 'self.verb_inflections'
+        self._prepare_wiktionary_verb_inflections(dl_manager)
+        
+        corpus_base_path = Path(dl_manager.download_and_extract(_URLs["qanom_zip"]))        
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": corpus_base_path / "annot.train.csv",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": corpus_base_path / "annot.dev.csv",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": corpus_base_path / "annot.test.csv",
+                },
+            ),
+        ]
+
+    @classmethod
+    def span_from_str(cls, s:str):
+        start, end = s.split(":")
+        return [int(start), int(end)]
+    
+    def _generate_examples(self, filepath):
+
+        """ Yields examples from a 'annot.?.csv' file in QANom's format."""
+
+        df = pd.read_csv(filepath)
+        for counter, row in df.iterrows():
+            # Each record (row) in csv is a QA or is stating a predicate/non-predicate with no QAs
+            
+            # Prepare question (slots)
+            na_to_underscore = lambda s: "_" if pd.isna(s) else str(s)
+            question = [] if pd.isna(row.question) else list(map(na_to_underscore, [
+                    row.wh, row.aux, row.subj, row.verb_slot_inflection, row.obj, row.prep, row.obj2 
+                ])) + ['?']
+            # fix verb slot - replace with actual verb inflection, and prepend verb_prefix 
+            if question:
+                if row.verb_form in self.verb_inflections and not pd.isna(row.verb_slot_inflection):
+                    verb_surface = self.verb_inflections[row.verb_form][row.verb_slot_inflection] 
+                else:
+                    verb_surface = row.verb_form
+                if not pd.isna(row.verb_prefix):
+                    verb_surface = row.verb_prefix + " " + verb_surface
+                question[3] = verb_surface
+            answers = [] if pd.isna(row.answer) else row.answer.split("~!~")
+            answer_ranges = [] if pd.isna(row.answer_range) else [Qanom.span_from_str(s) for s in row.answer_range.split("~!~")]
+            
+            yield counter, {
+                "sentence": row.sentence,
+                "sent_id": row.qasrl_id,
+                "predicate_idx": row.target_idx,
+                "predicate": row.noun,
+                "is_verbal": row.is_verbal,
+                "verb_form": row.verb_form,
+                "question": question,
+                "answers": answers,
+                "answer_ranges": answer_ranges
+            }
+