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davis_pdp_raw.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors.
+#
+# 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.
+"""The Winograd Schema Challenge Dataset"""
+
+import xml.etree.ElementTree as ET
+
+import datasets
+
+
+_DESCRIPTION = """\
+A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is
+resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its
+resolution. The schema takes its name from a well-known example by Terry Winograd:
+
+> The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.
+
+If the word is ``feared'', then ``they'' presumably refers to the city council; if it is ``advocated'' then ``they''
+presumably refers to the demonstrators.
+"""
+
+_CITATION = """\
+@inproceedings{levesque2012winograd,
+  title={The winograd schema challenge},
+  author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},
+  booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},
+  year={2012},
+  organization={Citeseer}
+}
+"""
+
+_HOMPAGE = "https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html"
+_DOWNLOAD_URL = "https://cs.nyu.edu/~davise/papers/WinogradSchemas/PDPChallenge2016.xml"
+
+
+class WinogradWSCConfig(datasets.BuilderConfig):
+    """BuilderConfig for WinogradWSC."""
+
+    def __init__(self, *args, language=None, inds=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.inds = set(inds) if inds is not None else None
+
+    def is_in_range(self, id):
+        """Takes an index and tells you if it belongs to the configuration's subset"""
+        return id in self.inds if self.inds is not None else True
+
+
+class WinogradWSC(datasets.GeneratorBasedBuilder):
+    """The Winograd Schema Challenge Dataset"""
+
+    BUILDER_CONFIG_CLASS = WinogradWSCConfig
+    BUILDER_CONFIGS = [
+        WinogradWSCConfig(
+            name="davis_pdp",
+            description="Full set of winograd examples",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "pronoun": datasets.Value("string"),
+                    "pronoun_loc": datasets.Value("int32"),
+                    "quote": datasets.Value("string"),
+                    "quote_loc": datasets.Value("int32"),
+                    "options": datasets.Sequence(datasets.Value("string")),
+                    "label": datasets.Value("int32"),
+                    "humanSubjects": datasets.Value("string"),
+                    "source": datasets.Value("string"),
+                }
+            ),
+            homepage=_HOMPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.download_and_extract(_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": path}),
+        ]
+
+    def _cleanup_whitespace(self, text):
+        return " ".join(text.split())
+
+    def _generate_examples(self, filepath):
+        tree = ET.parse(filepath)
+        for id, schema in enumerate(tree.getroot()):
+            if not self.config.is_in_range(id):
+                continue
+
+            text_root = schema.find("text")
+            quote_root = schema.find("quote")
+
+            text_left = self._cleanup_whitespace(text_root.findtext("txt1", ""))
+            text_right = self._cleanup_whitespace(text_root.findtext("txt2", ""))
+            quote_left = self._cleanup_whitespace(quote_root.findtext("quote1", ""))
+            quote_right = self._cleanup_whitespace(quote_root.findtext("quote2", ""))
+            pronoun = self._cleanup_whitespace(text_root.findtext("pron"))
+
+            features = {}
+            features["text"] = " ".join([text_left, pronoun, text_right]).strip()
+            features["quote"] = " ".join([quote_left, pronoun, quote_right]).strip()
+
+            features["pronoun"] = pronoun
+            features["options"] = [
+                self._cleanup_whitespace(option.text) for option in schema.find("answers").findall("answer")
+            ]
+
+            answer_txt = self._cleanup_whitespace(schema.findtext("correctAnswer"))
+            features["label"] = 0 if "A" in answer_txt else (1 if "B" in answer_txt else (2 if "C" in answer_txt else 3))  # convert "  A. " or " B " strings to a 0/1 index
+
+            features["pronoun_loc"] = len(text_left) + 1 if len(text_left) > 0 else 0
+            features["quote_loc"] = features["pronoun_loc"] - (len(quote_left) + 1 if len(quote_left) > 0 else 0)
+            features["humanSubjects"] = self._cleanup_whitespace(schema.findtext("humanSubjects"))
+            features["source"] = self._cleanup_whitespace(schema.findtext("source"))
+
+            yield id, features