Japanese translation teaching corpus based on bilingual non parallel data model

This paper firstly summarizes the tense expressions in Japanese, Chinese and English according to the cognitive linguistics theory so as to derive the grammatical transformation law in the process of Japanese to Chinese or Japanese to English. Secondly, on the basis of artificial intelligence technology, a hierarchical Japanese phrase model based on maximum entropy tense classification features is proposed, and the extraction of Japanese phrase rules is realized through maximum entropy mathematical modeling to obtain the model feature function and decoding strategy, and the application of the model in Japanese language teaching is explained in the research. Then, the second-year students of Japanese language major in the School of Foreign Studies, Nanjing University of Posts and Telecommunications are selected as the research objects, research tools and experimental process are determined, and simulation analysis and statistical analysis are used to analyze the example of Japanese language teaching with the integration of artificial intelligence. The results show that on the model, compared with the baseline, the model in this paper improves the blue value by 0.64%, the comprehensibility by 4.39%, and the tense accuracy by 4.14% on the whole test set, which verifies the effectiveness of the hierarchical phrase model fused with the tense features proposed in this paper in Japanese-Chinese translation. In terms of Japanese language teaching, the independent samples t-test p-values were less than 0.05, and the students in class A outperformed the students in class B in the four question types: multiple choice, sentence ordering, Chinese-to-Japanese translation and composition. This study improves students’ language proficiency and achieves the objective of developing high-quality talents.

Section: Related Researchmentioning

confidence: 99%

The Application of Cognitive Linguistics Theory in Japanese Language Teaching in the Age of Artificial Intelligence

Shan

2023

“…It has been proven through practical applications that this technology can facilitate cultural communication and understanding. Literature [15] constructs a Japanese translation teaching corpus based on a bilingual non-parallel data model and trains the MOSES translation model through the corpus so that it has a good translation retrieval performance and improves the BLEU value extracted from parallel sentences by 2.58. Both Japanese translation and translation teaching are positively impacted by the above method.…”

Section: Introductionmentioning

confidence: 99%

“…Xiaoting He and Liangliang Shi. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] …”

mentioning

confidence: 99%

A Study of the Assistive Nature of Artificial Intelligence Technology for Japanese Translation and Interpretation

He,

Shi

2024

Traditional Japanese translation methods have certain disadvantages, and the introduction of artificial intelligence technology into them can enhance the effect of Japanese interpretation and translation. In this paper, the Japanese language data of Twitter and Facebook are used as the basis to construct a Japanese language interpretation and translation corpus, and the GPT-2 model is constructed on the basis of Transformer for Japanese text translation. To optimize the Seq2Seq model for Japanese speech interpretation, the Attention mechanism is introduced to establish a Japanese speech translation model. A Japanese oral and written corpus was used to analyze the validity of the methods mentioned above. The results show that the class/form ratio in the Japanese oral/translated corpus fluctuates between [0.1231, 0.1448], but the survival rate of borrowed words under the scientific category reaches the highest of 54.14%, and the average number of occurrences of each word is between [4.35, 4.95]. Japanese verbal and translated texts had an average sentence length of 40 hours, and their translation accuracy was approximately 74.16%. The quality of translation can be effectively improved, and cultural exchange between China and Japan can be enhanced by integrating AI technology with Japanese interpretation and translation.

“…Moreover, along with the increasing number of highly educated researchers, the overall research strength has been greatly strengthened, especially with the rapid growth of young scholars, who have begun to become an important force for Japanese language research in China [3][4][5]. As far as the quality of research is concerned, there has been a significant improvement with the efforts of the majority of researchers, and there are more and more articles with originality and academic value, which is encouraging [6][7].…”

Section: Introductionmentioning

confidence: 99%

The Construction of a Grammatical System for Japanese Linguistics in the Context of Big Data

Zhao

2023

This paper first outlines the concept of teaching Japanese grammar according to the Japanese language talent cultivation standard and the current dilemma in teaching Japanese language in colleges and universities. Secondly, on the basis of big data technology, considering the effective availability of the initial data of Japanese grammar, it is necessary to preprocess the language grammar learning data and, at the same time, according to the fuzzy comprehensive evaluation, determine the Japanese grammar evaluation affiliation function. Then, for the problem of an incomplete and inaccurate traditional fuzzy comprehensive evaluation, they proposed to construct a comprehensive evaluation model of the Japanese linguistics grammar system based on the bat algorithm and carried out research and analysis on the Japanese linguistics grammar system. The results show that on the evaluation model, the comprehensive evaluation score of the Japanese language grammar system is C=2.65, which belongs to the E2 level. On the statistical analysis, the mean value of all the scores of the concise class was higher than that of the standard Japanese class, and all of them showed significant differences (p=0.002, 0.000, and 0.047, all less than 0.05).