Construction of Translation Corpus and Training of Translation Models Supported by Big Data

The rapid development of globalization and the expansion of comprehensive cross-border communication have made machine translation play an increasingly prominent role in achieving communication and understanding between different languages. However, traditional translation models based on Statistical Machine Translation (SMT) have certain limitations when facing complex and diverse language phenomena. With the expansion of big data technology applications, this study utilized these massive amounts of text data to construct more accurate and flexible translation models. This study aimed to complete the task of constructing a translation corpus and training translation models. Firstly, bilingual data from different fields and themes were collected. Then, automatic alignment techniques were used to model language correspondence and remove low-quality alignment based on filtering techniques. The next step was to preprocess the collected raw data, which included processes such as word segmentation and tokenization. The purpose of these steps was to enable the processed data to be used to train the model. Finally, a sequence-to-sequence (Seq2Seq) translation model, such as an encoder decoder architecture and attention mechanism, was used to train the data. Through experimental evaluation, this study found that translation corpora and neural network translation models based on big data support have achieved better performance in translation quality. Compared to traditional translation models, the method adopted in this study can better capture complex language structures and semantic information, providing more accurate and fluent translation results.