Incorporating Translation Quality Estimation into Chinese-Korean Neural Machine Translation

Li, Feiyu; Zhao, Yue; Yang, Feiyang; Cui, Rongyi

doi:10.1007/978-3-030-84186-7_4

Cited by 6 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advancement of ancient Korean translation based on machine-translation would improve the productivity of current classical translators and expedite the process [7]. In the process of machine learning known as feature extraction, relevant input data is chosen and transformed to provide useful features that may be used to train a model [8]. The application of machine translation technology to assess the quality of Korean translations produced by computers or translation software is known as machine Korean Translation Grading.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Feature Extraction Model for Machine Korean Translation Judging System

Piao

2024

jes

View full text Add to dashboard Cite

Language translation is becoming increasingly widespread and diversified as internationalization and information technology advance significantly. Nonetheless, there are cultural variations across different nations, and there will unavoidably be some variations brought on by translation software. While the machine translation system has attained a high level of development, the issues with the older machine translation methods still remain and cannot be fully resolved. The efficiency of Korean-Chinese translation must thus be improved by creating a more effective model for converting translation information. This has significant implications for the development of international relationships, commercial interactions, cultural development, and commerce between China and South Korea. The translation is the rendering of words into other languages so that they may be understood by both individuals. Nevertheless, there are several flaws and inconsistencies in the present Korean-to-Chinese translational information processing method. Thus, this study suggests a Diverse Deep Embedding Data Accumulation (DDEDA) algorithm for the translation framework. We utilized the Korean-Chinese corpus dataset for the research. Utilizing translation accuracy, precision, recall, error rate, and translation time, the performance of the suggested approach was examined and compared with previously employed methodologies. The findings demonstrate that the proposed method has taken 43s for translation.

show abstract

Section: Introductionmentioning

confidence: 99%

Analysis of Feature Extraction Model for Machine Korean Translation Judging System

Piao

2024

jes

View full text Add to dashboard Cite

show abstract

“…It is a fundamental technique for various tasks and has been successfully applied in many areas of natural language processing. For example, reading comprehension [1], question and answer systems [2], and machine translation [3]. In machine reading comprehension tasks, the correct answer can be selected by semantic matching between the context and the question.…”

Section: Introductionmentioning

confidence: 99%