Datasets:
Tasks:
Question Answering
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
License:
File size: 5,288 Bytes
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and 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.
# Lint as: python3
"""MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics"""
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{Chen2020MOCHAAD,
author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics},
booktitle={EMNLP},
year={2020}
}
"""
_DESCRIPTION = """\
Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for \
open-ended questions with few restrictions on possible answers. However, progress is impeded by existing \
generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. \
To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: \
MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from \
6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, \
we train an evaluation metric: LERC, a Learned Evaluation metric for Reading Comprehension, to mimic human \
judgement scores.
"""
_HOMEPAGE = "https://allennlp.org/mocha"
_LICENSE = "https://creativecommons.org/licenses/by-sa/4.0/legalcode"
_URL = "https://github.com/anthonywchen/MOCHA/raw/main/data/mocha.tar.gz"
_MINIMAL_PAIRS_SPLIT = "minimal_pairs"
SPLIT_FILENAMES = {
datasets.Split.TRAIN: "train.json",
datasets.Split.VALIDATION: "dev.json",
datasets.Split.TEST: "test_no_labels.json",
_MINIMAL_PAIRS_SPLIT: "minimal_pairs.json",
}
class Mocha(datasets.GeneratorBasedBuilder):
"""MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics"""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"constituent_dataset": datasets.Value("string"),
"id": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"reference": datasets.Value("string"),
"candidate": datasets.Value("string"),
"score": datasets.Value("float"),
"metadata": {
"scores": datasets.features.Sequence(datasets.Value("int32")),
"source": datasets.Value("string"),
},
# features for minimal pairs
"candidate2": datasets.Value("string"),
"score2": datasets.Value("float"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
archive = dl_manager.download(_URL)
return [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"filepath": "mocha/" + SPLIT_FILENAMES[split],
"split": split,
"files": dl_manager.iter_archive(archive),
},
)
for split in SPLIT_FILENAMES
]
def _generate_examples(self, filepath, split, files):
"""This function returns the examples in the raw (text) form."""
for path, f in files:
if path == filepath:
mocha = json.load(f)
for constituent_dataset, samples in mocha.items():
for id_, sample in samples.items():
sample["id"] = id_
sample["constituent_dataset"] = constituent_dataset
# Add default values
if split == _MINIMAL_PAIRS_SPLIT:
sample["candidate"] = sample["candidate1"]
sample["score"] = sample["score1"]
del sample["candidate1"], sample["score1"]
sample["metadata"] = {"scores": [], "source": ""}
else:
if "score" not in sample:
sample["score"] = -1.0
sample["metadata"]["scores"] = []
sample["candidate2"] = ""
sample["score2"] = -1.0
yield id_, sample
break
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