Grammatical Error Correction
Grammatical error correction (GEC) aims to automatically identify and rectify grammatical mistakes in text, primarily focusing on improving accuracy and fluency. Current research heavily utilizes large language models (LLMs) within various architectures, including sequence-to-sequence and non-autoregressive models, often employing techniques like data augmentation and multi-task learning to enhance performance. This field is significant for its potential to improve language learning tools, automate writing assistance, and advance the understanding of both human language and artificial intelligence evaluation methods. Furthermore, the development of robust evaluation metrics, including those leveraging LLMs themselves, is a key area of ongoing investigation.
Papers
Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods
Mengsay Loem, Masahiro Kaneko, Sho Takase, Naoaki Okazaki
Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora
Svanhvít Lilja Ingólfsdóttir, Pétur Orri Ragnarsson, Haukur Páll Jónsson, Haukur Barri Símonarson, Vilhjálmur Þorsteinsson, Vésteinn Snæbjarnarson