Optimization of deep learning-based Japanese machine translation models

The process of Japanese to English translation is not an easy one due to the fact that it is marred by the complex nature of the grammar system, the application of different scripts, and reliance on context. The typical neural machine translators (NMT), such as standard Long Short Term Memory (LSTM) and Transformer, can be described as struggling with long-range correlations and out-of-vocabulary issues to achieve the best performance. To address these weaknesses, a more direct attention-based encoder-decoder LSTM trained with Grizzly Bear Fat Increase Optimization (AED-LSTM-GBFI) is proposed, which is a more efficient encoder-decoder hyperparameter optimization method and more accurate translation. This training model is based on a Japanese-English Translation Dataset, which has 4200 formal and informal aligned pairs of sentences. Data is also preprocessed by normalizing scripts and symbols and tokenizing using subword-level segmentation to address vocabulary sparsity. Embedding layers are employed to feature extract, in which Global Vectors of Word Representation (Glove) are fed on English tokens, and subword embeddings (SE) are fed on Japanese tokens, which ensures good semantic coverage. The programming language of Python, TensorFlow, and PyTorch are used to perform the training and evaluation. The outcome of the experimental results of the AED-LSTM-GBFI model that converges faster and achieves a higher score in the Bilingual Evaluation Understudy (BLEU) 35.92 and Character n-gram F-score (ChrF) 64.87. The proposed framework provides an effective solution in terms of scalability, interpretability, and computational efficiency in Japanese-English machine translation, showing the promise of bio-inspired optimization methods in further improving the efficacy of deep learning models in NLP.