First version of qa_srl2020 datasets script

qa_srl2020.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 QASRL-GS dataset (Roit et. al., ACL 2020)."""
+
+
+import datasets
+from pathlib import Path
+from typing import List
+import pandas as pd
+
+
+_CITATION = """\
+@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, a.k.a "QASRL-GS" (Gold Standard) or "QASRL-2020", was constructed via controlled crowdsourcing.
+See the paper for details: Controlled Crowdsourcing for High-Quality QA-SRL Annotation, Roit et. al., 2020 
+"""
+
+_HOMEPAGE = "https://github.com/plroit/qasrl-gs"
+
+_LICENSE = """MIT License
+
+Copyright (c) 2020 plroit
+
+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 = {
+    "sentences": {
+        "wikinews.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikinews.dev.full.csv",
+        "wikinews.test": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikinews.test.full.csv",
+        "wikipedia.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikipedia.dev.full.csv",
+        "wikipedia.test": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikipedia.test.full.csv",
+    },
+    "qasrl-annotations": {
+        "wikinews.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikinews.dev.gold.csv",
+        "wikinews.test": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikinews.test.gold.csv",
+        "wikipedia.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikipedia.dev.gold.csv",
+        "wikipedia.test": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikipedia.test.gold.csv",
+    },        
+}
+
+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 QaSrl2020(datasets.GeneratorBasedBuilder):
+    """QA-SRL2020: Question-Answer driven SRL gold-standard dataset.
+    Notice: This dataset genrally follows the format of `qa_srl` and `kleinay\qa_srl2018` datasets. 
+    However, it extends Features to include "is_verbal" and "verb_form" fields, as in the `kleinay\qanom` dataset that accounts for nominalizations.
+    Nevertheless these fields can be ignored, since for all data points in QASRL-2020, "is_verbal"==True and "verb_form" is equivalent to the "predicate" feature.  """
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain_text", version=VERSION, description="This provides the QASRL-2020 (QASRL-GS) 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)
+        
+        # Download and prepare all files - keep same structure as _URLs 
+        corpora = {data_type: {       
+                section:  Path(dl_manager.download_and_extract(_URLs[data_type][section]))
+                for section in _URLs[data_type] } 
+            for data_type in _URLs
+        }
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "qasrl_annotations_paths": [corpora["qasrl-annotations"]["wikinews.dev"], 
+                                                corpora["qasrl-annotations"]["wikipedia.dev"]],
+                    "sentences_paths": [corpora["sentences"]["wikinews.dev"],
+                                        corpora["sentences"]["wikipedia.dev"]],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "qasrl_annotations_paths": [corpora["qasrl-annotations"]["wikinews.test"], 
+                                                corpora["qasrl-annotations"]["wikipedia.test"]],
+                    "sentences_paths": [corpora["sentences"]["wikinews.test"],
+                                        corpora["sentences"]["wikipedia.test"]],
+                },
+            ),
+        ]
+
+    @classmethod
+    def span_from_str(cls, s:str):
+        start, end = s.split(":")
+        return [int(start), int(end)]
+    
+    def _generate_examples(self, qasrl_annotations_paths: List[str], sentences_paths: List[str]):
+
+        """ Yields QASRL examples from a csv file in QASRL-2020/QANom format."""
+
+        # merge sentence and create a map to raw-sentence from sentence-id 
+        sent_df = pd.concat([pd.read_csv(fn) for fn in sentences_paths])
+        qasrl_id2sent = {r["qasrl_id"]: r["sentence"] for _, r in sent_df.iterrows()}
+        # merge annotations from sections 
+        df = pd.concat([pd.read_csv(fn) for fn in qasrl_annotations_paths]).reset_index()
+        for counter, row in df.iterrows():
+            # Each record (row) in csv is a QA or is stating a predicate/non-predicate with no QAs
+            sentence = qasrl_id2sent[row.qasrl_id]
+            # 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 in self.verb_inflections and not pd.isna(row.verb_slot_inflection):
+                    verb_surface = self.verb_inflections[row.verb][row.verb_slot_inflection] 
+                else:
+                    verb_surface = row.verb
+                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 [QaSrl2020.span_from_str(s) for s in row.answer_range.split("~!~")]
+            
+            yield counter, {
+                "sentence": sentence,
+                "sent_id": row.qasrl_id,
+                "predicate_idx": row.verb_idx,
+                "predicate": row.verb,
+                "is_verbal": True,
+                "verb_form": row.verb,
+                "question": question,
+                "answers": answers,
+                "answer_ranges": answer_ranges
+            }
+