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

• URL: http://arxiv.org/abs/2503.19702v1
• Date: Tue, 25 Mar 2025 14:29:43 GMT
• Title: HausaNLP at SemEval-2025 Task 2: Entity-Aware Fine-tuning vs. Prompt Engineering in Entity-Aware Machine Translation
• Authors: Abdulhamid Abubakar, Hamidatu Abdulkadir, Ibrahim Rabiu Abdullahi, Abubakar Auwal Khalid, Ahmad Mustapha Wali, Amina Aminu Umar, Maryam Bala, Sani Abdullahi Sani, Ibrahim Said Ahmad, Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Vukosi Marivate,
• Abstract summary: This paper presents our findings for SemEval 2025 Task 2, a shared task on entity-aware machine translation (EA-MT)<n>The goal of this task is to develop translation models that can accurately translate English sentences into target languages.<n>In this paper, we describe the different systems we employed, detail our results, and discuss insights gained from our experiments.
• Score: 2.17880235420183
• License: http://creativecommons.org/licenses/by/4.0/
• 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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