@inproceedings{abubakar-etal-2025-hausanlp,
title = "{H}ausa{NLP} at {S}em{E}val-2025 Task 2: Entity-Aware Fine-tuning vs. Prompt Engineering in Entity-Aware Machine Translation",
author = "Abubakar, Abdulhamid and
Abdulkadir, Hamidatu and
Ibrahim, Rabiu and
Auwal, Abubakar and
Wali, Ahmad and
Umar, Amina and
Bala, Maryam and
Sani, Sani Abdullahi and
Ahmad, Ibrahim Said and
Muhammad, Shamsuddeen Hassan and
Abdulmumin, Idris and
Marivate, Vukosi",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.120/",
pages = "885--892",
ISBN = "979-8-89176-273-2",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T HausaNLP at SemEval-2025 Task 2: Entity-Aware Fine-tuning vs. Prompt Engineering in Entity-Aware Machine Translation
%A Abubakar, Abdulhamid
%A Abdulkadir, Hamidatu
%A Ibrahim, Rabiu
%A Auwal, Abubakar
%A Wali, Ahmad
%A Umar, Amina
%A Bala, Maryam
%A Sani, Sani Abdullahi
%A Ahmad, Ibrahim Said
%A Muhammad, Shamsuddeen Hassan
%A Abdulmumin, Idris
%A Marivate, Vukosi
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F abubakar-etal-2025-hausanlp
%X 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.
%U https://aclanthology.org/2025.semeval-1.120/
%P 885-892
Markdown (Informal)
[HausaNLP at SemEval-2025 Task 2: Entity-Aware Fine-tuning vs. Prompt Engineering in Entity-Aware Machine Translation](https://aclanthology.org/2025.semeval-1.120/) (Abubakar et al., SemEval 2025)
ACL
- Abdulhamid Abubakar, Hamidatu Abdulkadir, Rabiu Ibrahim, Abubakar Auwal, Ahmad Wali, Amina Umar, Maryam Bala, Sani Abdullahi Sani, Ibrahim Said Ahmad, Shamsuddeen Hassan Muhammad, Idris Abdulmumin, and Vukosi Marivate. 2025. HausaNLP at SemEval-2025 Task 2: Entity-Aware Fine-tuning vs. Prompt Engineering in Entity-Aware Machine Translation. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 885–892, Vienna, Austria. Association for Computational Linguistics.