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Found 6 papers, 2 papers with code
Neural Machine Translation of Clinical Text: An Empirical Investigation into Multilingual Pre-Trained Language Models and Transfer-Learning
Furthermore, to address the language resource imbalance issue, we also carry out experiments using a transfer learning methodology based on massive multilingual pre-trained language models (MMPLMs).
Predictive Data Analytics with AI: assessing the need for post-editing of MT output by fine-tuning OpenAI LLMs
Translation Quality Evaluation (TQE) is an essential step of the modern translation production process.
Investigating Massive Multilingual Pre-Trained Machine Translation Models for Clinical Domain via Transfer Learning
To the best of our knowledge, this is the first work on using MMPLMs towards \textit{clinical domain transfer-learning NMT} successfully for totally unseen languages during pre-training.
Examining Large Pre-Trained Language Models for Machine Translation: What You Don't Know About It
Pre-trained language models (PLMs) often take advantage of the monolingual and multilingual dataset that is freely available online to acquire general or mixed domain knowledge before deployment into specific tasks.
Measuring Uncertainty in Translation Quality Evaluation (TQE)
From both human translators (HT) and machine translation (MT) researchers' point of view, translation quality evaluation (TQE) is an essential task.
cushLEPOR: customising hLEPOR metric using Optuna for higher agreement with human judgments or pre-trained language model LaBSE
Then we present the customised hLEPOR (cushLEPOR) which uses Optuna hyper-parameter optimisation framework to fine-tune hLEPOR weighting parameters towards better agreement to pre-trained language models (using LaBSE) regarding the exact MT language pairs that cushLEPOR is deployed to.
