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With the recent developments in the field of Natural Language Processing,there has been a rise in the use of different architectures for Neural MachineTranslation. Transformer architectures are used to achieve state-of-the-artaccuracy, but they are very computationally expensive to train. Everyone cannothave such setups consisting of high-end GPUs and other resources. We train ourmodels on low computational resources and investigate the results. As expected,transformers outperformed other architectures, but there were some surprisingresults. Transformers consisting of more encoders and decoders took more timeto train but had fewer BLEU scores. LSTM performed well in the experiment andtook comparatively less time to train than transformers, making it suitable touse in situations having time constraints.

Analyzing Architectures for Neural Machine Translation Using Low Computational Resources