Datasets:
Tasks:
Multiple Choice
Sub-tasks:
multiple-choice-coreference-resolution
Languages:
English
Size:
n<1K
ArXiv:
License:
File size: 4,618 Bytes
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"""A modification of the Winograd Schema Challenge to ensure answers are a single context word"""
from __future__ import absolute_import, division, print_function
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
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