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+ # coding=utf-8
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+ # Copyright 2020 The HuggingFace Datasets Authors.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """The Winograd Schema Challenge Dataset"""
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+
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+ import xml.etree.ElementTree as ET
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+
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+ import datasets
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+
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+
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+ _DESCRIPTION = """\
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+ A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is
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+ resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its
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+ resolution. The schema takes its name from a well-known example by Terry Winograd:
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+
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+ > The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.
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+
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+ If the word is ``feared'', then ``they'' presumably refers to the city council; if it is ``advocated'' then ``they''
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+ presumably refers to the demonstrators.
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+ """
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+
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+ _CITATION = """\
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+ @inproceedings{levesque2012winograd,
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+ title={The winograd schema challenge},
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+ author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},
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+ booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},
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+ year={2012},
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+ organization={Citeseer}
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+ }
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+ """
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+
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+ _HOMPAGE = "https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html"
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+ _DOWNLOAD_URL = "https://cs.nyu.edu/~davise/papers/WinogradSchemas/PDPChallenge2016.xml"
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+
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+
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+ class WinogradWSCConfig(datasets.BuilderConfig):
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+ """BuilderConfig for WinogradWSC."""
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+
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+ def __init__(self, *args, language=None, inds=None, **kwargs):
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+ super().__init__(*args, **kwargs)
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+ self.inds = set(inds) if inds is not None else None
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+
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+ def is_in_range(self, id):
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+ """Takes an index and tells you if it belongs to the configuration's subset"""
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+ return id in self.inds if self.inds is not None else True
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+
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+
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+ class WinogradWSC(datasets.GeneratorBasedBuilder):
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+ """The Winograd Schema Challenge Dataset"""
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+
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+ BUILDER_CONFIG_CLASS = WinogradWSCConfig
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+ BUILDER_CONFIGS = [
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+ WinogradWSCConfig(
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+ name="davis_pdp",
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+ description="Full set of winograd examples",
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+ ),
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+ ]
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features(
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+ {
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+ "text": datasets.Value("string"),
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+ "pronoun": datasets.Value("string"),
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+ "pronoun_loc": datasets.Value("int32"),
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+ "quote": datasets.Value("string"),
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+ "quote_loc": datasets.Value("int32"),
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+ "options": datasets.Sequence(datasets.Value("string")),
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+ "label": datasets.ClassLabel(num_classes=2),
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+ "source": datasets.Value("string"),
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+ }
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+ ),
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+ homepage=_HOMPAGE,
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ path = dl_manager.download_and_extract(_DOWNLOAD_URL)
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": path}),
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+ ]
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+
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+ def _cleanup_whitespace(self, text):
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+ return " ".join(text.split())
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+
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+ def _generate_examples(self, filepath):
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+ tree = ET.parse(filepath)
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+ for id, schema in enumerate(tree.getroot()):
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+ if not self.config.is_in_range(id):
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+ continue
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+
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+ text_root = schema.find("text")
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+ quote_root = schema.find("quote")
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+
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+ text_left = self._cleanup_whitespace(text_root.findtext("txt1", ""))
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+ text_right = self._cleanup_whitespace(text_root.findtext("txt2", ""))
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+ quote_left = self._cleanup_whitespace(quote_root.findtext("quote1", ""))
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+ quote_right = self._cleanup_whitespace(quote_root.findtext("quote2", ""))
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+ pronoun = self._cleanup_whitespace(text_root.findtext("pron"))
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+
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+ features = {}
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+ features["text"] = " ".join([text_left, pronoun, text_right]).strip()
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+ features["quote"] = " ".join([quote_left, pronoun, quote_right]).strip()
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+
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+ features["pronoun"] = pronoun
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+ features["options"] = [
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+ self._cleanup_whitespace(option.text) for option in schema.find("answers").findall("answer")
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+ ]
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+
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+ answer_txt = self._cleanup_whitespace(schema.findtext("correctAnswer"))
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+ features["label"] = int("B" in answer_txt) # convert " A. " or " B " strings to a 0/1 index
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+
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+ features["pronoun_loc"] = len(text_left) + 1 if len(text_left) > 0 else 0
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+ features["quote_loc"] = features["pronoun_loc"] - (len(quote_left) + 1 if len(quote_left) > 0 else 0)
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+ features["source"] = self._cleanup_whitespace(schema.findtext("source"))
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+
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+ yield id, features