Abstract Grammatical Error Correction (GEC) is the task of automatically detecting and
correcting errors in text. The task not only includes the correction of grammatical errors, such …

Currently, there is little agreement as to how Natural Language Generation (NLG) systems
should be evaluated, with a particularly high degree of variation in the way that human …

This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC).
As with the CoNLL-2014 shared task, participants are required to correct all types of errors in …

This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese
Grammatical Error Correction (CGEC), consisting of 7,063 sentences collected from three …

Grammatical error correction (GEC) is an important application aspect of natural language
processing techniques, and GEC system is a kind of very important intelligent system that …

The increasing versatility of language models LMs has given rise to a new class of
benchmarks that comprehensively assess a broad range of capabilities. Such benchmarks …

Until now, most of the research in grammar error correction focused on English, and the
problem has hardly been explored for other languages. We address the task of correcting …

Neural sequence-to-sequence (seq2seq) approaches have proven to be successful in
grammatical error correction (GEC). Based on the seq2seq framework, we propose a novel …

The learning trajectories of linguistic phenomena in humans provide insight into linguistic
representation, beyond what can be gleaned from inspecting the behavior of an adult …

Large language models (eg, GPT-4) are uniquely capable of producing highly rated text
simplification, yet current human evaluation methods fail to provide a clear understanding of …