arXiv Open Access 2025

Enhancing Entity Aware Machine Translation with Multi-task Learning

An Trieu Phuong Nguyen Minh Le Nguyen

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Abstrak

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.

Topik & Kata Kunci

cs.CL

Penulis (3)

An Trieu

Phuong Nguyen

Minh Le Nguyen

Format Sitasi

APA MLA BibTeX

Trieu, A., Nguyen, P., Nguyen, M.L. (2025). Enhancing Entity Aware Machine Translation with Multi-task Learning. https://arxiv.org/abs/2506.18318

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓