Research on the Application of Translation Parallel Corpus in Interpretation Teaching

Abstract

Large and organized sets of translated texts between languages are called parallel translation corpora (PTLs). Even though data-driven learning can generate insights from massive datasets and create more tailored learning experiences, it has gained in popularity. There are however a few problems with this strategy, such as poor data quality, privacy concerns, inability to scale, a lack of clear explanations, and high costs. To provide high-quality output, machine translation algorithms are generally trained utilizing parallel corpora generated by human translators. The written word can be deciphered from one language to another through translation. The spoken word can be conveyed from one language to another through translation. Based on the study of real samples, machine translation from corpus linguistics uses its translations to create its translations. Statistical approaches are only one of the many ways a corpus may be used. Translated texts from two or more languages are called parallel corpora. With the emergence of data-driven learning (DDL) in translation training and language instruction, they are becoming increasingly popular in translation and contrastive research. While working as a professional translator, you're likely to encounter a wide range of challenges. These are lexical-semantic, grammar, syntactic, rhetorical, practical, and cultural difficulties. There are limitations to the number of possible translations that dictionaries can provide and difficulty in doing a thorough search. When it comes to translating, contemporary technologies bring up a whole new world of possibilities thanks to the sheer volume of data and the speed at which it is available. Students of translation can benefit from this study's innovative way of employing PTL-DDL, which can help them improve the quality and speed of their translations. In addition, the parallel corpora's sample sentences are readily available, making it easier to choose the best translations from a pool of translation candidates. Because of these characteristics, the approach is well-suited to creating active or encoding dictionaries.