Investigating Techniques for a Deeper Understanding of Neural Machine Translation (NMT) Systems through Data Filtering and Fine-tuning Strategies

AbstractIn the context of this biomedical shared task, we have implemented data filters to enhance the selection of relevant training data for fine- tuning from the available training data sources. Specifically, we have employed textometric analysis to detect repetitive segments within the test set, which we have then used for re- fining the training data used to fine-tune the mBart-50 baseline model. Through this approach, we aim to achieve several objectives: developing a practical fine-tuning strategy for training biomedical in-domain fr<>en models, defining criteria for filtering in-domain training data, and comparing model predictions, fine-tuning data in accordance with the test set to gain a deeper insight into the functioning of Neural Machine Translation (NMT) systems.