In an increasingly interconnected world, the ability to accurately translate text from one language to another is of utmost importance. This project aims to develop an attention-based neural machine translation model specifically designed for translating German text to English. By utilizing the widely-used WMT https://huggingface.co/datasets/wmt16/viewer/de-en dataset, which provides a rich collection of parallel German-English sentence pairs, we can train and evaluate the model to achieve accurate and contextually-aware translations.
Central to this project is the implementation of attention mechanisms, both global and local, within the neural machine translation model. These mechanisms enhance the model's ability to capture important information during the translation process.
The model architecture comprises an encoder and a decoder. The encoder processes the German sentence, capturing its semantic meaning and transforming it into a distributed representation. During the decoding phase, both global and local attention mechanisms come into play.
Global attention allows the decoder to attend to all parts of the encoded German sentence, dynamically weighting the importance of each word or phrase. This mechanism helps the model align relevant information across source and target sentences.
To know more about the attention mechanism refer to -Effective Approaches to Attention-based Neural Machine Translation- https://aclanthology.org/D15-1166/
In addition, we incorporate local attention, which focuses on a narrower context window within the encoded German sentence. This enables the model to place emphasis on specific regions of interest while generating the English translation. By utilizing both global and local attention, the model can improve translation accuracy and fluency.
Central to this project is the implementation of attention mechanisms, both global and local, within the neural machine translation model. These mechanisms enhance the model's ability to capture important information during the translation process.
The model architecture comprises an encoder and a decoder. The encoder processes the German sentence, capturing its semantic meaning and transforming it into a distributed representation. During the decoding phase, both global and local attention mechanisms come into play.
Global attention allows the decoder to attend to all parts of the encoded German sentence, dynamically weighting the importance of each word or phrase. This mechanism helps the model align relevant information across source and target sentences.
In addition, we incorporate local attention, which focuses on a narrower context window within the encoded German sentence. This enables the model to place emphasis on specific regions of interest while generating the English translation. By utilizing both global and local attention, the model can improve translation accuracy and fluency.