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

<p>With the rapid advancement of scientific and technological innovation, the significance of intelligent translation services has grown considerably. This study leverages policy support to integrate AI technology into the domain of grammar error detection and correction. It utilizes a Long Short-Term Memory (LSTM) neural network and employs a Transformer model grounded in a self-attention mechanism to address the task of grammar error correction. This approach is designed to balance local contextual nuances and long-range dependencies within the text, culminating in the development of an AI-based grammar error correction model. A thorough evaluation of this model’s performance involved comparative analysis with alternative models, assessments across various types of syntactic errors, and detection of collocation errors. The findings indicate that our model exhibits superior grammatical error correction capabilities, outperforming comparative models. Specifically, it achieves Grammar Learning Evaluation Understudy (GLEU) scores that are 2.31% to 7.95% higher than those of its counterparts. Moreover, it demonstrates overall recognition rates for different grammatical and collocational errors between 18.88% to 58.98% and 50% to 70%, respectively, which underscore its practical applicability. This methodology not only enhances grammatical error detection but also holds promise for broader application in AI-driven translation services.</p>
</abstract>

With the rapid advancement of scientific and technological innovation, the significance of intelligent translation services has grown considerably. This study leverages policy support to integrate AI technology into the domain of grammar error detection and correction. It utilizes a Long Short-Term Memory (LSTM) neural network and employs a Transformer model grounded in a self-attention mechanism to address the task of grammar error correction. This approach is designed to balance local contextual nuances and long-range dependencies within the text, culminating in the development of an AI-based grammar error correction model. A thorough evaluation of this model’s performance involved comparative analysis with alternative models, assessments across various types of syntactic errors, and detection of collocation errors. The findings indicate that our model exhibits superior grammatical error correction capabilities, outperforming comparative models. Specifically, it achieves Grammar Learning Evaluation Understudy (GLEU) scores that are 2.31% to 7.95% higher than those of its counterparts. Moreover, it demonstrates overall recognition rates for different grammatical and collocational errors between 18.88% to 58.98% and 50% to 70%, respectively, which underscore its practical applicability. This methodology not only enhances grammatical error detection but also holds promise for broader application in AI-driven translation services.

Policy Perspectives on Big Data and AI in Translation Services

China University of Geosciences & Wuchang Shouyi University

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Policy Perspectives on Big Data and AI in Translation Services"}

With the rapid advancement of scientific and technological innovation, the significance of intelligent translation services has grown considerably. This study...

Policy Perspectives on Big Data and AI in Translation Services

Online veröffentlicht: 05. Juli 2024

Seitenbereich: -

Eingereicht: 09. März 2024

Akzeptiert: 31. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1611

Schlüsselwörter
Self-attention mechanism, LSTM neural network, Transformer, Grammatical error correction, AI translation

© 2024 Hui Yu, published by Sciendo

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

Policy Perspectives on Big Data and AI in Translation Services

Online veröffentlicht: 05. Juli 2024

Seitenbereich: -

Eingereicht: 09. März 2024

Akzeptiert: 31. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1611

SchlüsselwörterSelf-attention mechanism, LSTM neural network, Transformer, Grammatical error correction, AI translation

© 2024 Hui Yu, published by Sciendo

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

Schlüsselwörter
Self-attention mechanism, LSTM neural network, Transformer, Grammatical error correction, AI translation