Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
# coding=utf-8 | |
# 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. | |
"""A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications""" | |
import glob | |
import json | |
import os | |
import datasets | |
_CITATION = """\ | |
@inproceedings{kang18naacl, | |
title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications}, | |
author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz}, | |
booktitle = {Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL)}, | |
address = {New Orleans, USA}, | |
month = {June}, | |
url = {https://arxiv.org/abs/1804.09635}, | |
year = {2018} | |
} | |
""" | |
_DESCRIPTION = """\ | |
PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers. | |
""" | |
_HOMEPAGE = "https://github.com/allenai/PeerRead" | |
_LICENSE = "Creative Commons Public License" | |
_URLs = { | |
"dataset_repo": "https://github.com/allenai/PeerRead/archive/master.zip", | |
} | |
class PeerRead(datasets.GeneratorBasedBuilder): | |
"""A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications""" | |
VERSION = datasets.Version("1.1.0") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="parsed_pdfs", | |
version=VERSION, | |
description="Research paper drafts", | |
), | |
datasets.BuilderConfig( | |
name="reviews", | |
version=VERSION, | |
description="Accept/reject decisions in top-tier venues including ACL, NIPS and ICLR", | |
), | |
] | |
def _get_paths(data_dir, domain): | |
paths = {"train": [], "test": [], "dev": []} | |
conference_paths = glob.glob(os.path.join(data_dir, "PeerRead-master/data/*")) | |
for conference_path in conference_paths: | |
for dtype in ["test", "train", "dev"]: | |
file_paths = glob.glob(os.path.join(conference_path, dtype, domain, "*.json")) | |
for file_path in file_paths: | |
paths[dtype].append(file_path) | |
return paths | |
def _parse_histories(histories): | |
if histories is None: | |
return [[]] | |
if isinstance(histories, str): | |
return [[histories]] | |
return histories | |
def _parse_reviews(data): | |
reviews = [] | |
for review in data.get("metadata", {}).get("reviews", []): | |
if isinstance(review, dict): | |
reviews.append( | |
{ | |
"date": str(review.get("date", "")), | |
"title": str(review.get("title", "")), | |
"other_keys": str(review.get("other_keys", "")), | |
"originality": str(review.get("originality", "")), | |
"comments": str(review.get("comments", "")), | |
"is_meta_review": str(review.get("is_meta_review", "")), | |
"is_annotated": str(review.get("is_annotated", "")), | |
"recommendation": str(review.get("recommendation", "")), | |
"replicability": str(review.get("replicability", "")), | |
"presentation_format": str(review.get("presentation_format", "")), | |
"clarity": str(review.get("clarity", "")), | |
"meaningful_comparison": str(review.get("meaningful_comparison", "")), | |
"substance": str(review.get("substance", "")), | |
"reviewer_confidence": str(review.get("reviewer_confidence", "")), | |
"soundness_correctness": str(review.get("soundness_correctness", "")), | |
"appropriateness": str(review.get("appropriateness", "")), | |
"impact": str(review.get("impact")), | |
} | |
) | |
return reviews | |
def _decode(text): | |
return str(text).encode("utf-8", "replace").decode("utf-8") | |
def _info(self): | |
if ( | |
self.config.name == "parsed_pdfs" | |
): # This is the name of the configuration selected in BUILDER_CONFIGS above | |
features = datasets.Features( | |
{ | |
"name": datasets.Value("string"), | |
"metadata": { | |
"source": datasets.Value("string"), | |
"title": datasets.Value("string"), | |
"authors": datasets.features.Sequence(datasets.Value("string")), | |
"emails": datasets.features.Sequence(datasets.Value("string")), | |
"sections": datasets.features.Sequence( | |
{ | |
"heading": datasets.Value("string"), | |
"text": datasets.Value("string"), | |
} | |
), | |
"references": datasets.features.Sequence( | |
{ | |
"title": datasets.Value("string"), | |
"author": datasets.features.Sequence(datasets.Value("string")), | |
"venue": datasets.Value("string"), | |
"citeRegEx": datasets.Value("string"), | |
"shortCiteRegEx": datasets.Value("string"), | |
"year": datasets.Value("int32"), | |
} | |
), | |
"referenceMentions": datasets.features.Sequence( | |
{ | |
"referenceID": datasets.Value("int32"), | |
"context": datasets.Value("string"), | |
"startOffset": datasets.Value("int32"), | |
"endOffset": datasets.Value("int32"), | |
} | |
), | |
"year": datasets.Value("int32"), | |
"abstractText": datasets.Value("string"), | |
"creator": datasets.Value("string"), | |
}, | |
} | |
) | |
else: | |
features = datasets.Features( | |
{ | |
"id": datasets.Value("string"), | |
"conference": datasets.Value("string"), | |
"comments": datasets.Value("string"), | |
"subjects": datasets.Value("string"), | |
"version": datasets.Value("string"), | |
"date_of_submission": datasets.Value("string"), | |
"title": datasets.Value("string"), | |
"authors": datasets.features.Sequence(datasets.Value("string")), | |
"accepted": datasets.Value("bool"), | |
"abstract": datasets.Value("string"), | |
"histories": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))), | |
"reviews": datasets.features.Sequence( | |
{ | |
"date": datasets.Value("string"), | |
"title": datasets.Value("string"), | |
"other_keys": datasets.Value("string"), | |
"originality": datasets.Value("string"), | |
"comments": datasets.Value("string"), | |
"is_meta_review": datasets.Value("bool"), | |
"is_annotated": datasets.Value("bool"), | |
"recommendation": datasets.Value("string"), | |
"replicability": datasets.Value("string"), | |
"presentation_format": datasets.Value("string"), | |
"clarity": datasets.Value("string"), | |
"meaningful_comparison": datasets.Value("string"), | |
"substance": datasets.Value("string"), | |
"reviewer_confidence": datasets.Value("string"), | |
"soundness_correctness": datasets.Value("string"), | |
"appropriateness": datasets.Value("string"), | |
"impact": datasets.Value("string"), | |
} | |
), | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
url = _URLs["dataset_repo"] | |
data_dir = dl_manager.download_and_extract(url) | |
paths = self._get_paths( | |
data_dir=data_dir, | |
domain=self.config.name, | |
) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepaths": paths["train"], | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={"filepaths": paths["test"], "split": "test"}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"filepaths": paths["dev"], | |
"split": "dev", | |
}, | |
), | |
] | |
def _generate_examples(self, filepaths, split): | |
"""Yields examples.""" | |
for id_, filepath in enumerate(sorted(filepaths)): | |
with open(filepath, encoding="utf-8", errors="replace") as f: | |
data = json.load(f) | |
if self.config.name == "parsed_pdfs": | |
metadata = data.get( | |
"metadata", | |
{ | |
"source": "", | |
"authors": [], | |
"title": [], | |
"sections": [], | |
"references": [], | |
"referenceMentions": [], | |
"year": "", | |
"abstractText": "", | |
"creator": "", | |
}, | |
) | |
metadata["sections"] = [] if metadata["sections"] is None else metadata["sections"] | |
metadata["sections"] = [ | |
{ | |
"heading": self._decode(section.get("heading", "")), | |
"text": self._decode(section.get("text", "")), | |
} | |
for section in metadata["sections"] | |
] | |
metadata["references"] = [] if metadata["references"] is None else metadata["references"] | |
metadata["references"] = [ | |
{ | |
"title": reference.get("title", ""), | |
"author": reference.get("author", []), | |
"venue": reference.get("venue", ""), | |
"citeRegEx": reference.get("citeRegEx", ""), | |
"shortCiteRegEx": reference.get("shortCiteRegEx", ""), | |
"year": reference.get("year", ""), | |
} | |
for reference in metadata["references"] | |
] | |
metadata["referenceMentions"] = ( | |
[] if metadata["referenceMentions"] is None else metadata["referenceMentions"] | |
) | |
metadata["referenceMentions"] = [ | |
{ | |
"referenceID": self._decode(reference_mention.get("referenceID", "")), | |
"context": self._decode(reference_mention.get("context", "")), | |
"startOffset": self._decode(reference_mention.get("startOffset", "")), | |
"endOffset": self._decode(reference_mention.get("endOffset", "")), | |
} | |
for reference_mention in metadata["referenceMentions"] | |
] | |
yield id_, { | |
"name": data.get("name", ""), | |
"metadata": metadata, | |
} | |
elif self.config.name == "reviews": | |
yield id_, { | |
"id": str(data.get("id", "")), | |
"conference": str(data.get("conference", "")), | |
"comments": str(data.get("comments", "")), | |
"subjects": str(data.get("subjects", "")), | |
"version": str(data.get("version", "")), | |
"date_of_submission": str(data.get("date_of_submission", "")), | |
"title": str(data.get("title", "")), | |
"authors": data.get("authors", []) | |
if isinstance(data.get("authors"), list) | |
else ([data.get("authors")] if data.get("authors") else []), | |
"accepted": str(data.get("accepted", "")), | |
"abstract": str(data.get("abstract", "")), | |
"histories": self._parse_histories(data.get("histories", [])), | |
"reviews": self._parse_reviews(data), | |
} | |