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

<p>In today’s increasingly frequent cultural exchanges between China and Japan, accurate and error-free Japanese translation has become an inevitable choice for cross-cultural communication. In this paper, based on twin neural network and attention mechanism, BiLSTM model is combined with sentence semantic similarity matching algorithm to construct a Japanese translation bias sentence semantic similarity model. The Japanese corpus data were collected and preprocessed by Python technology, and the Japanese translation corpus database was searched and counted using Wordsmith and AntConc tools. For the Japanese learners’ translation bias in the Japanese translation process, a comparative analysis was carried out in several aspects, such as end-of-sentence modal expressions, consecutive translations, and word frequency effects. The study results show that the difference in the frequency distribution of Japanese learners’ modal expressions is only 4.66% compared with that of native speakers of Japanese. Still, the difference between the two is significant at the 1% level, and the difference in the frequency of Japanese learners’ use of the modal expression “yes” is 56 sentences per 10,000 sentences. The frequency of Japanese learners’ use of successive expressions was 30.1 percentage points higher than that of native speakers. The neural semantic analysis method combined with the Japanese translation corpus can clarify the translation bias of Japanese learners in the process of Japanese translation, which can provide a reference for enhancing the translation quality of Japanese learning.</p>
</abstract>

In today’s increasingly frequent cultural exchanges between China and Japan, accurate and error-free Japanese translation has become an inevitable choice for cross-cultural communication. In this paper, based on twin neural network and attention mechanism, BiLSTM model is combined with sentence semantic similarity matching algorithm to construct a Japanese translation bias sentence semantic similarity model. The Japanese corpus data were collected and preprocessed by Python technology, and the Japanese translation corpus database was searched and counted using Wordsmith and AntConc tools. For the Japanese learners’ translation bias in the Japanese translation process, a comparative analysis was carried out in several aspects, such as end-of-sentence modal expressions, consecutive translations, and word frequency effects. The study results show that the difference in the frequency distribution of Japanese learners’ modal expressions is only 4.66% compared with that of native speakers of Japanese. Still, the difference between the two is significant at the 1% level, and the difference in the frequency of Japanese learners’ use of the modal expression “yes” is 56 sentences per 10,000 sentences. The frequency of Japanese learners’ use of successive expressions was 30.1 percentage points higher than that of native speakers. The neural semantic analysis method combined with the Japanese translation corpus can clarify the translation bias of Japanese learners in the process of Japanese translation, which can provide a reference for enhancing the translation quality of Japanese learning.

A Comparative Analysis of Japanese Learners’ Translation Bias Using Neurosemantic Analysis

Shanghai Zhongqiao Vocational and Technial University

Applied Mathematics and Nonlinear Sciences

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

In today’s increasingly frequent cultural exchanges between China and Japan, accurate and error-free Japanese translation has become an inevitable choice for...

A Comparative Analysis of Japanese Learners’ Translation Bias Using Neurosemantic Analysis

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 12. Jan. 2024

Akzeptiert: 19. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0550

Schlüsselwörter
Twin neural network, Attention mechanism, BiLSTM model, Semantic similarity, Translation bias

© 2023 Zheng Cao, published by Sciendo

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

A Comparative Analysis of Japanese Learners’ Translation Bias Using Neurosemantic Analysis

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 12. Jan. 2024

Akzeptiert: 19. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0550

SchlüsselwörterTwin neural network, Attention mechanism, BiLSTM model, Semantic similarity, Translation bias

© 2023 Zheng Cao, published by Sciendo

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

Schlüsselwörter
Twin neural network, Attention mechanism, BiLSTM model, Semantic similarity, Translation bias