Research on Translation Style in Machine Learning Based on Linguistic Quantitative Characteristics Perception

Abstract

Research on the metrological characteristics of linguistic quantitative characteristics (LQCs) based on corpus and metrological linguistic methods has gained wide attention in artificial and online machine translations. Although a support vector machine (SVM) is one of the most widely used machine learning (ML) algorithms in the field of text analysis, its application in the study of translation style is rare. This study compares the translation styles of Pride and Prejudice with ML using different linguistic measurement features. Firstly, the language measurement features of three translations are obtained with the information gain algorithm. Specifically, the corpus can be achieved through human-machine interaction (HCI), i.e., computers can look, hear, touch, smell, taste, and speak using sensors such as cameras and mathematical algorithms. Then a text classifier, i.e., an SVM, is constructed on the basis of these features to automatically classify the translated texts of the three translations. Finally, the validity of the classifier is verified by the tenfold cross-validation method. It is proved that the SVM algorithm has high classification accuracy and a strong predictive function, which is helpful for judging or predicting the translation or translator's style. Compared with the traditional method, this classification method based on an SVM saves time and effort, the process can be repeated, and the result is accurate and reliable.

A Corpus-Based Comparative Study of Translators’ Style: with Seven Versions of Hetang Yuese as Examples

“…After the corpus was applied to the field of translation research in the 1990s, people were no longer limited to the perceptual understanding of the translation, but began to use the powerful computing power of the computer to calculate the relevant data of the translation and analyze the characteristics of translation. Previous studies mainly focus on translation teaching, lexicography [5], corpus construction [10], translation universality [2] [8], and translator's style [4]. Baker points out that translation studies has inherited from literary studies its preoccupation with the style of individual creative writers and from linguistics the preoccupation with the style of social groups of language users [3].…”

Algorithmic Translation Correction Mechanisms: An End-to-end Algorithmic Implementation of English-Chinese Machine Translation

“…The processing result of each module affects the performance of the final machine translation system. With the development of deep learning technology, deep learning has gradually been adopted to train multilevel neural networks to perform specified tasks [9][10]. Progress has been made in processing natural language, such as machine translation, question-andanswer systems, reading, and comprehension.…”