HausaNLP at SemEval-2025 Task 2: Entity-Aware Fine-tuning vs. Prompt Engineering in Entity-Aware Machine Translation
Not on the map. Why?
Abstract (arXiv)
This paper presents our findings for SemEval 2025 Task 2, a shared task on entity-aware machine translation (EA-MT). The goal of this task is to develop translation models that can accurately translate English sentences into target languages, with a particular focus on handling named entities, which often pose challenges for MT systems. The task covers 10 target languages with English as the source. In this paper, we describe the different systems we employed, detail our results, and discuss insights gained from our experiments.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 15 references that are in the map. First posted on arXiv 25 Mar 2025.
Builds on
5 of 15 references.
References · 5
- 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
- 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
- 2024Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphscited by 22
- 2024Correcting FLORES Evaluation Dataset for Four African Languagescited by 13
- 2023Extract and Attend: Improving Entity Translation in Neural Machine Translationcited by 2
Built on by
Not cited yet.