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README.md

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----
-annotations_creators:
-- expert-generated
-language:
-- en
-language_creators:
-- expert-generated
-license:
-- cc-by-4.0
-multilinguality:
-- monolingual
-pretty_name: Modified Winograd Schema Challenge (MWSC)
-size_categories:
-- n<1K
-source_datasets:
-- extended|winograd_wsc
-task_categories:
-- multiple-choice
-task_ids:
-- multiple-choice-coreference-resolution
-paperswithcode_id: null
-dataset_info:
-  features:
-  - name: sentence
-    dtype: string
-  - name: question
-    dtype: string
-  - name: options
-    sequence: string
-  - name: answer
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 11022
-    num_examples: 80
-  - name: test
-    num_bytes: 15220
-    num_examples: 100
-  - name: validation
-    num_bytes: 13109
-    num_examples: 82
-  download_size: 19197
-  dataset_size: 39351
----
-
-# Dataset Card for The modified Winograd Schema Challenge (MWSC)
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [http://decanlp.com](http://decanlp.com)
-- **Repository:** https://github.com/salesforce/decaNLP
-- **Paper:** [The Natural Language Decathlon: Multitask Learning as Question Answering](https://arxiv.org/abs/1806.08730)
-- **Point of Contact:** [Bryan McCann](mailto:[email protected]), [Nitish Shirish Keskar](mailto:[email protected])
-- **Size of downloaded dataset files:** 19.20 kB
-- **Size of the generated dataset:** 39.35 kB
-- **Total amount of disk used:** 58.55 kB
-
-### Dataset Summary
-
-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.
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Dataset Structure
-
-### Data Instances
-
-#### default
-
-- **Size of downloaded dataset files:** 0.02 MB
-- **Size of the generated dataset:** 0.04 MB
-- **Total amount of disk used:** 0.06 MB
-
-An example looks as follows:
-```
-{
-    "sentence": "The city councilmen refused the demonstrators a permit because they feared violence.",
-    "question": "Who feared violence?",
-    "options": [ "councilmen", "demonstrators" ],
-    "answer": "councilmen"
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### default
-- `sentence`: a `string` feature.
-- `question`: a `string` feature.
-- `options`: a `list` of `string` features.
-- `answer`: a `string` feature.
-
-### Data Splits
-
-| name  |train|validation|test|
-|-------|----:|---------:|---:|
-|default|   80|        82| 100|
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-Our code for running decaNLP has been open sourced under BSD-3-Clause. 
-
-We chose to restrict decaNLP to datasets that were free and publicly accessible for research, but you should check their individual terms if you deviate from this use case.
-
-From the [Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html):
-> Both versions of the collections are licenced under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
-
-### Citation Information
-
-If you use this in your work, please cite:
-```
-@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}
-}
-```
-
-
-### Contributions
-
-Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@ghomasHudson](https://github.com/ghomasHudson), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

dataset_infos.json

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-{"default": {"description": "Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.\nThis modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing.\n", "citation": "@article{McCann2018decaNLP,\n  title={The Natural Language Decathlon: Multitask Learning as Question Answering},\n  author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},\n  journal={arXiv preprint arXiv:1806.08730},\n  year={2018}\n}\n", "homepage": "http://decanlp.com", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "options": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "mwsc", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 11022, "num_examples": 80, "dataset_name": "mwsc"}, "test": {"name": "test", "num_bytes": 15220, "num_examples": 100, "dataset_name": "mwsc"}, "validation": {"name": "validation", "num_bytes": 13109, "num_examples": 82, "dataset_name": "mwsc"}}, "download_checksums": {"https://raw.githubusercontent.com/salesforce/decaNLP/1e9605f246b9e05199b28bde2a2093bc49feeeaa/local_data/schema.txt": {"num_bytes": 19197, "checksum": "31da9bee05796bbe0f6c957f54d1eb82eb5c644a8ee59f2ff1fa890eff3885dd"}}, "download_size": 19197, "dataset_size": 39351, "size_in_bytes": 58548}}

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mwsc.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