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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AELHEGRXZ" target="_blank" >RIV/00216208:11320/23:ELHEGRXZ - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/10.1145/3617372" target="_blank" >https://dl.acm.org/doi/10.1145/3617372</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3617372" target="_blank" >10.1145/3617372</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

"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."

Název v anglickém jazyce

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

Popis výsledku anglicky

"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."

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

"ACM Transactions on Asian and Low-Resource Language Information Processing"

ISSN

2375-4699

e-ISSN

—

Svazek periodika

""

Číslo periodika v rámci svazku

2023

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

1-17

Kód UT WoS článku

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EID výsledku v databázi Scopus

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