Improving Access to Medical Information for Multilingual Patients using Pipelined Ensemble Average based Machine Translation

Abstract

Machine translation has shown potential in improving access to medical information and healthcare services for multilingual patients. This research aims to enhance machine translation accuracy in the medical field, specifically for translating from Hindi to English. The study introduces a new approach that dynamically allocates decoding parameters using regression models, overcoming the limitations of fixed parameters in the decoder. A comprehensive dataset is created to address limited data availability, enabling regression models to predict optimal pruning parameters. The main motivation for the study is the introduction of a regression method for optimizing pruning parameters, which is a novel approach in this context. The proposed approach outperforms existing methods, achieving improved translation accuracy. Standard metrics such as the BLEU score are used to evaluate translations. Ensemble average and pipeline approaches further enhance performance. The improved performance of the proposed models can be attributed to the ensemble of diverse models (Extra Trees, LightGBM, XGBoost, and Random Forest) that employ various techniques to reduce overfitting, enhance prediction accuracy, and improve translation by correcting prediction errors. The study contributes to facilitating the translation and sharing of medical literature, promoting collaboration and knowledge exchange across languages. The research demonstrates the effectiveness of the regression method for optimizing pruning parameters in machine translation, leading to improved translation accuracy in the medical field. The proposed models offer promising results, paving the way for enhanced machine translation systems and promoting collaboration and knowledge exchange in the medical domain. The source code is available at https://huggingface.co/debajyoty/statistical-regression-Based-MT/tree/main/Statistical-Regression-SMT.