<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The way teachers correct errors in English teaching can cause problems such as psychological pressure, and deep learning technology offers the possibility of automatic error correction. In this paper, the final states of left and right texts are computed by constructing two attention mechanisms, target word-independent and related, and the merged obtained vectors are inputted into the RNN model for grammatical error recognition. In collocation error recognition, a Rank-based word candidate set ranking method is added, and error correction for verb usage is semantically encoded using RNN. The study was analyzed and tested in terms of grammar, collocation, and verbs. The ATT-RNN model accuracy is 3.62 percentage points higher than CAMB, and the difference in recall and <italic>F</italic><sub>0.5</sub>-value is not more than 0.5 percentage points, which indicates that the algorithmic model has some research value.</p>
</abstract>

The way teachers correct errors in English teaching can cause problems such as psychological pressure, and deep learning technology offers the possibility of automatic error correction. In this paper, the final states of left and right texts are computed by constructing two attention mechanisms, target word-independent and related, and the merged obtained vectors are inputted into the RNN model for grammatical error recognition. In collocation error recognition, a Rank-based word candidate set ranking method is added, and error correction for verb usage is semantically encoded using RNN. The study was analyzed and tested in terms of grammar, collocation, and verbs. The ATT-RNN model accuracy is 3.62 percentage points higher than CAMB, and the difference in recall and F0.5-value is not more than 0.5 percentage points, which indicates that the algorithmic model has some research value.

Application of Recurrent Neural Networks Based on Attentional Mechanisms in Classification Error Correction in English Teaching

Shanghai International Studies University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The way teachers correct errors in English teaching can cause problems such as psychological pressure, and deep learning technology offers the possibility of...

Application of Recurrent Neural Networks Based on Attentional Mechanisms in Classification Error Correction in English Teaching

Online veröffentlicht: 18. Nov. 2023

Seitenbereich: -

Eingereicht: 17. Jan. 2023

Akzeptiert: 29. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01174

Schlüsselwörter
Attention mechanism, RNN model, Semantic coding, Left and right text, Automatic error correction, English language teaching

© 2023 Chanjuan Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of Recurrent Neural Networks Based on Attentional Mechanisms in Classification Error Correction in English Teaching

Online veröffentlicht: 18. Nov. 2023

Seitenbereich: -

Eingereicht: 17. Jan. 2023

Akzeptiert: 29. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01174

SchlüsselwörterAttention mechanism, RNN model, Semantic coding, Left and right text, Automatic error correction, English language teaching

© 2023 Chanjuan Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Attention mechanism, RNN model, Semantic coding, Left and right text, Automatic error correction, English language teaching