Document-level grammatical error correction
Zheng Yuan, Christopher Bryant
2021Workshop on Innovative Use of NLP for Building Educational Applications12 citations
Abstract
Document-level context can provide valuable information in grammatical error correction (GEC), which is crucial for correcting certain errors and resolving inconsistencies. In this paper, we investigate context-aware approaches and propose document-level GEC systems. Additionally, we employ a three-step training strategy to benefit from both sentence-level and document-level data. Our system outperforms previous document-level and all other NMT-based single-model systems, achieving state of the art on a common test set.
Topics & Concepts
Computer scienceContext (archaeology)SentenceSet (abstract data type)Natural language processingArtificial intelligenceTest setInformation retrievalProgramming languageBiologyPaleontologyNatural Language Processing TechniquesTopic ModelingMultimodal Machine Learning Applications