Enhancing Entity Aware Machine Translation with Multi-task Learning

• URL: http://arxiv.org/abs/2506.18318v1
• Date: Mon, 23 Jun 2025 06:05:46 GMT
• Title: Enhancing Entity Aware Machine Translation with Multi-task Learning
• Authors: An Trieu, Phuong Nguyen, Minh Le Nguyen,
• Abstract summary: We propose a method that applies multi-task learning to optimize the performance of the two subtasks named entity recognition and machine translation.<n>The result and analysis are performed on the dataset provided by the organizer of Task 2 of the SemEval 2025 competition.
• Score: 2.9611509639584312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entity-aware machine translation (EAMT) is a complicated task in natural language processing due to not only the shortage of translation data related to the entities needed to translate but also the complexity in the context needed to process while translating those entities. In this paper, we propose a method that applies multi-task learning to optimize the performance of the two subtasks named entity recognition and machine translation, which improves the final performance of the Entity-aware machine translation task. The result and analysis are performed on the dataset provided by the organizer of Task 2 of the SemEval 2025 competition.

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