Tamil to English with Context Awareness for Homophones and Homonyms

In addressing the complex challenge of translating Tamil to English, particularly with the aim of accurately interpreting homophones and homonyms, this study implements Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) algorithms. These linguistic phenomena, where words sound alike or are spelled identically but have different meanings, pose significant obstacles in machine translation, potentially leading to erroneous interpretations. Our approach leverages the deep learning capabilities of LSTM to grasp the long-term dependencies and contextual nuances within sentences. Simultaneously, BERT's innovative mechanism for pre-training on a vast corpus and then fine-tuning for specific tasks allows it to excel in understanding the context, thereby distinguishing between the varied meanings of homophones and homonyms accurately. This research aims to enhance the precision of Tamil to English translations by incorporating context awareness, thereby significantly improving the quality and reliability of machine-translated text.

Keywords: Tamil to English translation, homophones, homonyms, machine translation, deep learning, LSTM, BERT, context awareness, natural language processing, accuracy, linguistic nuances.