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README.md

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----
-annotations_creators:
-- no-annotation
-language:
-- en
-- de
-- fr
-- ru
-language_creators:
-- machine-generated
-- expert-generated
-license:
-- mit
-multilinguality:
-- multilingual
-- translation
-pretty_name: Wino-X
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
-task_categories:
-- translation
-- coreference resolution
-- commonsense reasoning
-task_ids:
-- multiple-choice-qa
-- language-modeling
----
-
-# Dataset Card for Wino-X
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-instances)
-  - [Data Splits](#data-instances)
-- [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)
-
-## Dataset Description
-
-- **Homepage:** [Wino-X repository](https://github.com/demelin/Wino-X)
-- **Repository:** [Wino-X repository](https://github.com/demelin/Wino-X)
-- **Paper:** [Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution](https://aclanthology.org/2021.emnlp-main.670/)
-- **Leaderboard:** [N/A]
-- **Point of Contact:** [Denis Emelin](demelin.github.io)
-
-### Dataset Summary
-
-Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English
-counterparts, used to examine whether neural machine translation models can perform coreference resolution that
-requires commonsense knowledge, and whether multilingual language models are capable of commonsense reasoning across
-multiple languages.
-
-### Supported Tasks and Leaderboards
-
-- translation: The dataset can be used to evaluate translations of ambiguous source sentences, as produced by translation models . A [pretrained transformer-based NMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) can be used for this purpose.
-- coreference-resolution: The dataset can be used to rank alternative translations of an ambiguous source sentence that differ in the chosen referent of an ambiguous source pronoun. A [pretrained transformer-based NMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) can be used for this purpose.
-- commonsense-reasoning: The dataset can also be used evaluate whether pretrained multilingual language models can perform commonsense reasoning in (or across) multiple languages by identifying the correct filler in a cloze completion task. An [XLM-based model](https://huggingface.co/xlm-roberta-base) can be used for this purpose.
-
-### Languages
-
-The dataset (both its MT and LM portions) is available in the following translation pairs: English-German, English-French, English-Russian. All English sentences included in *Wino-X* were extracted from publicly available parallel corpora, as detailed in the accompanying paper, and represent the dataset-specific language varieties. All non-English sentences were obtained through machine translation and may, as such, exhibit features of translationese.
-
-## Dataset Structure
-
-### Data Instances
-
-The following represents a typical *MT-Wino-X* instance (for the English-German translation pair):  
-  
-{"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",  
-"sentence": "The woman looked for a different vase for the bouquet because it was too small.",  
-"translation1": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil sie zu klein war.",  
-"translation2": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil er zu klein war.",  
-"answer": 1,  
-"pronoun1": "sie",  
-"pronoun2": "er",  
-"referent1_en": "vase",  
-"referent2_en": "bouquet",  
-"true_translation_referent_of_pronoun1_de": "Vase",  
-"true_translation_referent_of_pronoun2_de": "Blumenstrauß",  
-"false_translation_referent_of_pronoun1_de": "Vase",  
- "false_translation_referent_of_pronoun2_de": "Blumenstrauß"}  
-  
-  
-The following represents a typical *LM-Wino-X* instance (for the English-French translation pair):  
-  
-{"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",   
-"sentence": "The woman looked for a different vase for the bouquet because it was too small.",   
-"context_en": "The woman looked for a different vase for the bouquet because _ was too small.",   
-"context_fr": "La femme a cherché un vase différent pour le bouquet car _ était trop petit.",   
-"option1_en": "the bouquet",   
-"option2_en": "the vase",   
-"option1_fr": "le bouquet",   
-"option2_fr": "le vase",   
-"answer": 2,   
-"context_referent_of_option1_fr": "bouquet",  
-"context_referent_of_option2_fr": "vase"}
-
-### Data Fields
-
-For *MT-Wino-X*:  
-  
-- "qID": Unique identifier ID for this dataset instance.
-- "sentence":  English sentence containing the ambiguous pronoun 'it'.
-- "translation1": First translation candidate.
-- "translation2": Second translation candidate.
-- "answer":  ID of the correct translation.
-- "pronoun1": Translation of the ambiguous source pronoun in translation1.
-- "pronoun2": Translation of the ambiguous source pronoun in translation2.
-- "referent1_en": English referent of the translation of the ambiguous source pronoun in translation1.
-- "referent2_en": English referent of the translation of the ambiguous source pronoun in translation2.
-- "true_translation_referent_of_pronoun1_[TGT-LANG]": Target language referent of pronoun1 in the correct translation.
-- "true_translation_referent_of_pronoun2_[TGT-LANG]":  Target language referent of pronoun2 in the correct translation.
-- "false_translation_referent_of_pronoun1_[TGT-LANG]": Target language referent of pronoun1 in the incorrect translation.
-- "false_translation_referent_of_pronoun2_[TGT-LANG]":  Target language referent of pronoun2 in the incorrect translation.
-  
-  
-For *LM-Wino-X*:  
-  
-- "qID": Unique identifier ID for this dataset instance.
-- "sentence": English sentence containing the ambiguous pronoun 'it'. 
-- "context_en": Same English sentence, where 'it' is replaced by a gap. 
-- "context_fr": Target language translation of the English sentence, where the translation of 'it' is replaced by a gap.
-- "option1_en": First filler option for the English sentence.
-- "option2_en":  Second filler option for the English sentence.
-- "option1_[TGT-LANG]": First filler option for the target language sentence.
-- "option2_[TGT-LANG]":  Second filler option for the target language sentence.
-- "answer": ID of the correct gap filler.
-- "context_referent_of_option1_[TGT-LANG]": English translation of option1_[TGT-LANG].
-- "context_referent_of_option2_[TGT-LANG]": English translation of option2_[TGT-LANG]
-
-### Data Splits
-
-*Wno-X* was designed as an evaluation-only benchmark and therefore is intended to be used for zero-shot testing only. However, users are very welcome to split the data as they wish :) .
-
-
-## Dataset Creation
-
-### Curation Rationale
-
-Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
-
-#### Who are the source language producers?
-
-Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
-
-### Annotations
-
-#### Annotation process
-
-Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
-
-#### Who are the annotators?
-
-Annotations were generated automatically and verified by the dataset author / curator for correctness.
-
-### Personal and Sensitive Information
-
-[N/A]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
-
-### Discussion of Biases
-
-Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
-
-### Other Known Limitations
-
-Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
-
-## Additional Information
-
-### Dataset Curators
-
-[Denis Emelin](demelin.github.io)
-
-### Licensing Information
-
-MIT
-
-### Citation Information
-
-@inproceedings{Emelin2021WinoXMW,
-  title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},
-  author={Denis Emelin and Rico Sennrich},
-  booktitle={EMNLP},
-  year={2021}
-}

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wino_x.py

@@ -1,168 +0,0 @@
-# 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.
-""" Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English
-counterparts, used to examine whether neural machine translation models can perform coreference resolution that
-requires commonsense knowledge, and whether multilingual language models are capable of commonsense reasoning across
-multiple languages. """
-
-import csv
-import json
-import os
-
-import datasets
-
-_CITATION = """\
-@inproceedings{Emelin2021WinoXMW,
-  title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},
-  author={Denis Emelin and Rico Sennrich},
-  booktitle={EMNLP},
-  year={2021}
-}
-"""
-
-# You can copy an official description
-_DESCRIPTION = """\
-Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English 
-counterparts, used to examine whether neural machine translation models can perform coreference resolution that 
-requires commonsense knowledge and whether multilingual language models are capable of commonsense reasoning across 
-multiple languages.
-"""
-
-_HOMEPAGE = "https://github.com/demelin/Wino-X"
-
-_LICENSE = "MIT"
-
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLS = {
-    "mt_en_de": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/mt/en_de_test.jsonl",
-    "mt_en_fr": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/mt/en_fr_test.jsonl",
-    "mt_en_ru": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/mt/en_ru_test.jsonl",
-    "lm_en_de": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/lm/en_de_test.jsonl",
-    "lm_en_fr": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/lm/en_fr_test.jsonl",
-    "lm_en_ru": "https://huggingface.co/datasets/demelin/wino_x/resolve/main/data/lm/en_ru_test.jsonl"
-}
-
-
-class WinoX(datasets.GeneratorBasedBuilder):
-    """ Wino-X is a dataset of German, French, and Russian Winograd schemas, aligned with their English counterparts """
-
-    VERSION = datasets.Version("1.1.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="mt_en_de", version=VERSION,
-                               description="This is the EN-DE part of the Wino-X translation data."),
-        datasets.BuilderConfig(name="mt_en_fr", version=VERSION,
-                               description="This is the EN-FR part of the Wino-X translation data."),
-        datasets.BuilderConfig(name="mt_en_ru", version=VERSION,
-                               description="This is the EN-RU part of the Wino-X translation data."),
-        datasets.BuilderConfig(name="lm_en_de", version=VERSION,
-                               description="This is the EN-DE part of the Wino-X language modeling data."),
-        datasets.BuilderConfig(name="lm_en_fr", version=VERSION,
-                               description="This is the EN-FR part of the Wino-X language modeling data."),
-        datasets.BuilderConfig(name="lm_en_ru", version=VERSION,
-                               description="This is the EN-RU part of the Wino-X language modeling data."),
-    ]
-
-    def _info(self):
-
-        # MT example:
-        # {"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",
-        # "sentence": "The woman looked for a different vase for the bouquet because it was too small.",
-        # "translation1": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil sie zu klein war.",
-        # "translation2": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil er zu klein war.",
-        # "answer": 1,
-        # "pronoun1": "sie",
-        # "pronoun2": "er",
-        # "referent1_en": "vase",
-        # "referent2_en": "bouquet",
-        # "true_translation_referent_of_pronoun1_de": "Vase",
-        # "true_translation_referent_of_pronoun2_de": "Blumenstrauß",
-        # "false_translation_referent_of_pronoun1_de": "Vase",
-        # "false_translation_referent_of_pronoun2_de": "Blumenstrauß"}
-
-        tgt_lang = self.config.name.split('_')[-1]
-        if self.config.name.startswith('mt_'):
-            features = datasets.Features(
-                {
-                    "qID": datasets.Value("string"),
-                    "sentence": datasets.Value("string"),
-                    "translation1": datasets.Value("string"),
-                    "translation2": datasets.Value("string"),
-                    "answer": datasets.Value("int64"),
-                    "pronoun1": datasets.Value("string"),
-                    "pronoun2": datasets.Value("string"),
-                    "referent1_en": datasets.Value("string"),
-                    "referent2_en": datasets.Value("string"),
-                    "true_translation_referent_of_pronoun1_{}".format(tgt_lang): datasets.Value("string"),
-                    "true_translation_referent_of_pronoun2_{}".format(tgt_lang): datasets.Value("string"),
-                    "false_translation_referent_of_pronoun1_{}".format(tgt_lang): datasets.Value("string"),
-                    "false_translation_referent_of_pronoun2_{}".format(tgt_lang): datasets.Value("string")
-                }
-            )
-
-        # LM example:
-        # {"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",
-        # "sentence": "The woman looked for a different vase for the bouquet because it was too small.",
-        # "context_en": "The woman looked for a different vase for the bouquet because _ was too small.",
-        # "context_de": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil _ zu klein war.",
-        # "option1_en": "the vase",
-        # "option2_en": "the bouquet",
-        # "option1_de": "die Vase",
-        # "option2_de": "der Blumenstrauß",
-        # "answer": 1,
-        # "context_referent_of_option1_de": "Vase",
-        # "context_referent_of_option2_de": "Blumenstrauß"}
-
-        else:
-            features = datasets.Features(
-                {
-                    "qID": datasets.Value("string"),
-                    "sentence": datasets.Value("string"),
-                    "context_en": datasets.Value("string"),
-                    "context_{}".format(tgt_lang): datasets.Value("string"),
-                    "answer": datasets.Value("int64"),
-                    "option1_en": datasets.Value("string"),
-                    "option2_en": datasets.Value("string"),
-                    "option1_{}".format(tgt_lang): datasets.Value("string"),
-                    "option2_{}".format(tgt_lang): datasets.Value("string"),
-                    "context_referent_of_option1_{}".format(tgt_lang): datasets.Value("string"),
-                    "context_referent_of_option2_{}".format(tgt_lang): 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,
-            # 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):
-        downloaded_files = dl_manager.download_and_extract(_URLS[self.config.name])
-        return [datasets.SplitGenerator(name=datasets.Split.TEST,
-                                        gen_kwargs={'filepath': downloaded_files, 'split': 'test'})]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                yield key, data