Inference

Knowledge representation and reasoning

Mathematics

Biology

Computer science

Management

Geography

Paleontology

Natural language processing

Machine learning

Economics

Generalization

Set (abstract data type)

SemEval

Sentence

Context (archaeology)

Commonsense knowledge

Geodesy

Benchmark (surveying)

Metric (unit)

Artificial intelligence

Generative model

Mathematical analysis

Commonsense reasoning

Programming language

Operations management

Task (project management)

Generative grammar

Recently, large-scale pre-trained language models have demonstrated impressive performance on several commonsense-reasoning benchmark datasets. However, building machines with commonsense to compose realistically plausible sentences remains challenging. In this paper, we present a constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts (e.g., dog, frisbee, catch, throw); the task is to generate a coherent sentence describing an everyday scenario using these concepts (e.g., “a man throws a frisbee and his dog catches it”). The CommonGen task is challenging because it inherently requires 1) relational reasoning with background commonsense knowledge and 2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through a combination of crowdsourced and existing caption corpora, consists of 77k commonsense descriptions over 35k uniqu...

CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning