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

Abstract

In the context of this biomedical shared task, we have implemented data filters to enhance the selection of relevant training data for finetuning 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 refining 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, finetuning data in accordance with the test set to gain a deeper insight into the functioning of Neural Machine Translation (NMT) systems.