This model adopted a dual-encoding structure composing of a syntactic encoder and a semantic encoder, and introduced a hybrid attention mechanism in the decoder to show that this model has good GEC performance and is suitable for English writing teaching.
Abstract
This paper developed a automatic grammar error correction (GEC) model based on the sequence-to-sequence (seq2seq) model. It adopted a dual-encoding structure composing of a syntactic encoder and a semantic encoder, and introduced a hybrid attention mechanism in the decoder. Finally, the results were output through the softmax layer. Experimental results on two general GEC test sets, CoNLL-2014 and JFLEG, showed that this model outperformed the existing comparison models in terms of precision, recall, and comprehensive evaluation indicator. In the error-correction test of actual English compositions written by students, the model successfully corrected more than half of the grammar errors marked manually. The results show that this model has good GEC performance and is suitable for English writing teaching.
With the continuous progress of artificial intelligence (AI) technology, the integrated application of intelligent translation and grammar error correction (GEC) model in English teaching has attracted more attention. As a key subtask of natural language processing (NLP), English GEC can provide automatic grammar corre...
Xue-Fei Jin· International Conference on...· 0 citations
A layered scheme linking computational Chinese grammatical error correction (CGEC) with pedagogical error analysis and a preliminary consistency study in which five large language models apply it to a sample support the layered approach while identifying category boundaries requiring further refinement.
Accurate, timely feedback on grammatical accuracy remains one of the most persistent challenges in English language teaching, particularly in large classes where teacher marking capacity is limited. This article reviews recent developments in the use of natural language processing (NLP) for the automated detection and...
Abba Alhaji Modu, Babagana Ali Dapshima, Fatima Saidu et al.· International Journal of Sci...· 0 citations
This study designs and implements a mixed evaluation platform for English composition that combines the Generative Language Model (GLMs) and teachers' wisdom, aiming at solving the problems of low efficiency, strong subjectivity and lack of feedback in traditional grading. The platform adopts a "two-layer evaluation fr...
Yuan-Wei Zhang, Fan-Rui Wei· International Conference on...· 0 citations
An end-to-end sequence-to-sequence formulation and fine-tune mT5-small and mBART-50 on a synthetic corpus of up to 657,720 noisy-clean Tamil sentence pairs spanning ten error categories, showing that a Tamil-adapted instruction model does not transfer to specialised sentence-level correction without task-specific super...
A. Karthikeyan, S. JayaNirmala, Sangeetha Sivanesan et al.· 0 citations
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