Investigating Syntactic Agreement Errors in Recurrent Networks and Humans

In this article, researchers propose a new benchmark called "Superglue" to evaluate the performance of general-purpose language understanding models. Superglue is designed to test how well these models can understand and process complex sentences with various structures, styles, and content. The benchmark consists of three types of instances: main clauses, complement clauses, and relative clauses. Each type presents a different challenge for the models, allowing them to demonstrate their ability to handle diverse language structures.
To create Superglue, the authors used a dataset of over 10 million sentences from various sources, including books, articles, and websites. They then manually selected and annotated a subset of these sentences as instances for each type of clause. The resulting benchmark consists of 100 examples for each type, with varying lengths and complexities.
The authors propose Superglue as an improvement over existing benchmarks because it focuses on understanding natural language rather than just processing individual words or phrases. Traditional benchmarks often rely on simple tasks such as word embeddings or language translation, which do not fully capture the complexity of real-world language use. In contrast, Superglue requires models to comprehend and process entire sentences, including their grammar, syntax, and semantics.
The authors also compare Superglue to other benchmarks, demonstrating that it provides a more challenging test for general-purpose language understanding models. They show that Superglue instances are more diverse and require better handling of complex sentence structures than existing benchmarks.
In summary, Superglue is a new benchmark designed to evaluate the ability of general-purpose language understanding models to comprehend and process complex sentences in natural language. It provides a more challenging test than existing benchmarks by presenting instances with varying lengths, structures, and content. By using Superglue, researchers can better understand how well these models perform in real-world language use and identify areas for improvement.