# HausaNLP at SemEval-2025 Task 2: Entity-Aware Fine-tuning vs. Prompt Engineering in Entity-Aware Machine Translation

arXiv: https://arxiv.org/abs/2503.19702 · HTML: https://mlsanity.com/paper/2503.19702/

## 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 (most cited first, 5 of 5)

- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs](https://mlsanity.com/paper/2410.14057/) (2024)
- [Correcting FLORES Evaluation Dataset for Four African Languages](https://mlsanity.com/paper/2409.00626/) (2024)
- [Extract and Attend: Improving Entity Translation in Neural Machine Translation](https://mlsanity.com/paper/2306.02242/) (2023)

## 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.