mlsanityData to 14 Oct 2026

Enhancing Entity Aware Machine Translation with Multi-task Learning

arXiv 2506.18318 ↗First posted 23 Jun 2025cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 13 references that are in the map. First posted on arXiv 23 Jun 2025.

Builds on

9 of 13 references.

References · 9

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2024Qwen2.5 Technical Reportcited by 4,942
  4. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  5. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
  6. 2021Word Alignment by Fine-tuning Embeddings on Parallel Corporacited by 186
  7. 2022Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answeringcited by 72
  8. 2024Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphscited by 22
  9. 2023Extract and Attend: Improving Entity Translation in Neural Machine Translationcited by 2

Built on by

Not cited yet.