Comparing Neural Architectures for English-Spanish Machine Translation: From LSTM to Transformer

This paper presents a systematic comparison of neural architectures for English-to-Spanish machine translation. We implement and evaluate five model configurations ranging from vanilla LSTM encoder-decoders to Transformer models with pretrained embeddings. Using the OPUS-100 corpus (1M training pairs) and FLORES+ benchmark (2,009 test pairs), we evaluate translation quality using BLEU, chrF, and COMET metrics. Our best Transformer model achieves a BLEU score of 20.26, closing approximately 65% of the performance gap between our strongest LSTM baseline (BLEU 10.66) and the state-of-the-art Helsinki-NLP model (BLEU 26.60). We analyze the impact of architectural choices, data scale, and pretrained embeddings on translation quality, providing insights into the trade-offs between model complexity and performance.