Viewing 1-10 of 50 datasets
- ZEST is a benchmark for zero-shot generalization to unseen NLP tasks, with 25K labeled instances across 1,251 different tasks.AI2 Irvine, Mosaic, AllenNLP • 2020ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include classification, typed entity extraction and relationship extraction, and each task is paired with 20 different annotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize in five different ways.
- A Dataset for Tracking Entities in Open Domain Procedural TextAristo, Mosaic • 2020We present the first dataset for tracking state changes in procedural text from arbitrary do-mains by using an unrestricted (open) vocabulary. For example, in a text describing fog removal using potatoes, a car window may transition between being foggy, sticky, opaque, and clear. Previous formulations of this task provide the text and entities involved, and ask how those entities change for just a small, pre-defined set of attributes (e.g., location), limiting their fidelity. Our solution is a new task formulation in which just the text is provided, from which a set of state changes (entity, at-tribute, before, after) is generated for each step, where the entity, attribute, and values must all be predicted from an open vocabulary. Using crowdsourcing, we create OPENPI, a high-quality (vetted by humans), and large-scale dataset comprising 33K state changes over 4,050 sentences from 810 procedural real-world paragraphs from WikiHow.com. An state-of-the-art generation model on this task achieves 18% F1 based on BLEU, leaving a lot of room for novel model architectures.
- 98k annotated explanations for the QASC datasetAristo • 2020This dataset contains 98k 2-hop explanations for questions in the QASC dataset, with annotations indicating if they are valid (~25k) or invalid (~73k) explanations.
- A high-quality KB of hasPart relationsAristo • 2020A high-quality knowledge base of ~50k hasPart relationships, extracted from a large corpus of generic statements.
- Academic paper representation dataset accompanying the SPECTER paper/modelSemantic Scholar • 2020Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, the embeddings power strong performance on end tasks. We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation.
- Datasets used to teach transformers to reasonAristo • 2020Can transformers be trained to reason (or emulate reasoning) over rules expressed in language? In the associated paper and demo we provide evidence that they can. Our models, that we call RuleTakers, are trained on datasets of synthetic rule bases plus derived conclusions, provided here. The resulting models provide the first demonstration that this kind of soft reasoning over language is indeed learnable.
- A large knowledge base of generic sentencesAristo • 2020The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as "Dogs bark," and "Trees remove carbon dioxide from the atmosphere." Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge. The GenericsKB is the first large-scale resource containing naturally occurring generic sentences (as opposed to extracted or crowdsourced triples), and is rich in high-quality, general, semantically complete statements. Generics were primarily extracted from three large text sources, namely the Waterloo Corpus, selected parts of Simple Wikipedia, and the ARC Corpus. A filtered, high-quality subset is also available in GenericsKB-Best, containing 1,020,868 sentences. We recommend you start with GenericsKB-Best.
- Tens of thousands of scholarly articles about COVID-19 and related coronavirusesSemantic Scholar • 2020CORD-19 is a free resource of tens of thousands of scholarly articles about COVID-19, SARS-CoV-2, and related coronaviruses for use by the global research community.
- 83,978 examples sampled from 10 question answering datasets over text, images and databases.AI2 Israel, Question Understanding • 2020Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations (QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases.
- The largest collection of machine-readable academic papers to date for NLP & text mining.Semantic Scholar • 2019A large corpus of 81.1M English-language academic papers spanning many academic disciplines. Rich metadata, paper abstracts, resolved bibliographic references, as well as structured full text for 8.1M open access papers. Full text annotated with automatically-detected inline mentions of citations, figures, and tables, each linked to their corresponding paper objects. Aggregated papers from hundreds of academic publishers and digital archives into a unified source, and create the largest publicly-available collection of machine-readable academic text to date.