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mwsc_raw.py

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+"""A modification of the Winograd Schema Challenge to ensure answers are a single context word"""
+
+import os
+import re
+
+import datasets
+
+
+_CITATION = """\
+@article{McCann2018decaNLP,
+  title={The Natural Language Decathlon: Multitask Learning as Question Answering},
+  author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},
+  journal={arXiv preprint arXiv:1806.08730},
+  year={2018}
+}
+"""
+
+_DESCRIPTION = """\
+Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.
+This modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing.
+"""
+
+_DATA_URL = "https://raw.githubusercontent.com/salesforce/decaNLP/1e9605f246b9e05199b28bde2a2093bc49feeeaa/local_data/schema.txt"
+# Alternate: https://s3.amazonaws.com/research.metamind.io/decaNLP/data/schema.txt
+
+
+class MWSC(datasets.GeneratorBasedBuilder):
+    """MWSC: modified Winograd Schema Challenge"""
+
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "options": datasets.features.Sequence(datasets.Value("string")),
+                    "answer": datasets.Value("string"),
+                }
+            ),
+            # 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="http://decanlp.com",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        schemas_file = dl_manager.download_and_extract(_DATA_URL)
+
+        if os.path.isdir(schemas_file):
+            # During testing the download manager mock gives us a directory
+            schemas_file = os.path.join(schemas_file, "schema.txt")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": schemas_file, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": schemas_file, "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": schemas_file, "split": "dev"},
+            ),
+        ]
+
+    def _get_both_schema(self, context):
+        """Split [option1/option2] into 2 sentences.
+        From https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L815-L827"""
+        pattern = r"\[.*\]"
+        variations = [x[1:-1].split("/") for x in re.findall(pattern, context)]
+        splits = re.split(pattern, context)
+        results = []
+        for which_schema in range(2):
+            vs = [v[which_schema] for v in variations]
+            context = ""
+            for idx in range(len(splits)):
+                context += splits[idx]
+                if idx < len(vs):
+                    context += vs[idx]
+            results.append(context)
+        return results
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+
+        schemas = []
+        with open(filepath, encoding="utf-8") as schema_file:
+            schema = []
+            for line in schema_file:
+                if len(line.split()) == 0:
+                    schemas.append(schema)
+                    schema = []
+                    continue
+                else:
+                    schema.append(line.strip())
+
+        # Train/test/dev split from decaNLP code
+        splits = {}
+        traindev = schemas[:-50]
+        splits["test"] = schemas[-50:]
+        splits["train"] = traindev[:40]
+        splits["dev"] = traindev[40:]
+
+        idx = 0
+        for schema in splits[split]:
+            sentence, question, answers = schema
+            sentence = self._get_both_schema(sentence)
+            question = self._get_both_schema(question)
+            answers = answers.split("/")
+            for i in range(2):
+                yield idx, {"sentence": sentence[i], "question": question[i], "options": answers, "answer": answers[i]}
+                idx += 1