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:
File size: 14,379 Bytes
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# 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",
),
]
@staticmethod
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
@staticmethod
def _parse_histories(histories):
if histories is None:
return [[]]
if isinstance(histories, str):
return [[histories]]
return histories
@staticmethod
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
@staticmethod
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),
}
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