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    <abstract>In 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\textless\textgreateren 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.</abstract>
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%0 Conference Proceedings
%T Investigating Techniques for a Deeper Understanding of Neural Machine Translation (NMT) Systems through Data Filtering and Fine-tuning Strategies
%A Zhu, Lichao
%A Zimina, Maria
%A Bénard, Maud
%A Namdar, Behnoosh
%A Ballier, Nicolas
%A Wisniewski, Guillaume
%A Yunès, Jean-Baptiste
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhu-etal-2023-investigating
%X In 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\textless\textgreateren 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.
%R 10.18653/v1/2023.wmt-1.28
%U https://aclanthology.org/2023.wmt-1.28
%U https://doi.org/10.18653/v1/2023.wmt-1.28
%P 275-281

Markdown (Informal)

[Investigating Techniques for a Deeper Understanding of Neural Machine Translation (NMT) Systems through Data Filtering and Fine-tuning Strategies](https://aclanthology.org/2023.wmt-1.28) (Zhu et al., WMT 2023)

• Investigating Techniques for a Deeper Understanding of Neural Machine Translation (NMT) Systems through Data Filtering and Fine-tuning Strategies (Zhu et al., WMT 2023)

ACL

• Lichao Zhu, Maria Zimina, Maud Bénard, Behnoosh Namdar, Nicolas Ballier, Guillaume Wisniewski, and Jean-Baptiste Yunès. 2023. Investigating Techniques for a Deeper Understanding of Neural Machine Translation (NMT) Systems through Data Filtering and Fine-tuning Strategies. In Proceedings of the Eighth Conference on Machine Translation, pages 275–281, Singapore. Association for Computational Linguistics.