upload qa_discourse.py script

qa_discourse.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 QA-Discourse dataset (Pyatkin et. al., ACL 2020)."""
+
+
+import datasets
+from pathlib import Path
+from typing import List
+import pandas as pd
+
+
+_CITATION = """\
+@inproceedings{pyatkin2020qadiscourse,
+  title={QADiscourse-Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines},
+  author={Pyatkin, Valentina and Klein, Ayal and Tsarfaty, Reut and Dagan, Ido},
+  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
+  pages={2804--2819},
+  year={2020}
+}"""
+
+
+_DESCRIPTION = """\
+The dataset contains question-answer pairs to model discourse relations. 
+While answers roughly correspond to spans of the sentence, these spans could have been freely adjusted by annotators to grammaticaly fit the question;
+Therefore, answers are given just as text and not as identified spans of the original sentence.    
+See the paper for details: QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines, Pyatkin et. al., 2020
+"""
+
+_HOMEPAGE = "https://github.com/ValentinaPy/QADiscourse"
+
+_LICENSE = """Resources on this page are licensed CC-BY 4.0, a Creative Commons license requiring Attribution (https://creativecommons.org/licenses/by/4.0/)."""
+
+
+_URLs = {
+    "wikinews.train": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_train.tsv",
+    "wikinews.dev": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_dev.tsv",
+    "wikinews.test": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_test.tsv",
+    "wikipedia.train": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_train.tsv",
+    "wikipedia.dev": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_dev.tsv",
+    "wikipedia.test": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_test.tsv",      
+}
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class QaDiscourse(datasets.GeneratorBasedBuilder):
+    """QA-Discourse: Discourse Relations as Question-Answer Pairs.  """
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain_text", version=VERSION, description="This provides the QA-Discourse 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"),
+                "question": datasets.Sequence(datasets.Value("string")),
+                "answers": datasets.Sequence(datasets.Value("string")),
+            }
+        )
+        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."""            
+        
+        # Download and prepare all files - keep same structure as _URLs 
+        corpora = {section:  Path(dl_manager.download_and_extract(_URLs[section])) 
+                   for section in _URLs} 
+            
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepaths": [corpora["wikinews.train"], 
+                                  corpora["wikipedia.train"]],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepaths": [corpora["wikinews.dev"], 
+                                  corpora["wikipedia.dev"]],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepaths": [corpora["wikinews.test"], 
+                                  corpora["wikipedia.test"]],
+                },
+            ),
+        ]
+    
+    def _generate_examples(self, filepaths: List[str]):
+
+        """ Yields QA-Discourse examples from a tsv file."""
+
+        # merge annotations from sections 
+        df = pd.concat([pd.read_csv(fn, separator='\t') for fn in filepaths]).reset_index()
+        for counter, row in df.iterrows():
+            # Prepare question (3 "slots" and question mark)
+            question = [row.question_start, row.question_aux, row.question_body.str.rstrip('?'), '?']
+            
+            yield counter, {
+                "sentence": row.sentence,
+                "sent_id": row.qasrl_id,
+                "question": question,
+                "answers": [row.answer],
+            }