# 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), }