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), | |
| } | |