@inproceedings{chen-2025-pingan,
title = "pingan-team at {S}em{E}val-2025 Task 2: {L}o{RA}-Augmented Qwen2.5 with {W}ikidata-Driven Entity Translation",
author = "Chen, Diyang",
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-1.268/",
pages = "2065--2070",
ISBN = "979-8-89176-273-2",
abstract = "This paper presents our solution for SemEval-2025 Task 2 on entity-aware machine translation. We propose a parameter-efficient adaptation framework using Low-Rank Adaptation (LoRA) to fine-tune the Qwen2.5-72B model, enabling effective knowledge transfer while preserving generalization capabilities. To address data scarcity and entity ambiguity, we design a Wiki-driven augmentation pipeline that leverages Wikidata{'}s multilingual entity mappings to generate synthetic training pairs. Our system achieves state-of-the-art performance across 10 languages, securing first place in the competition. Experimental results demonstrate significant improvements in both translation quality (COMET) and entity accuracy (M-ETA)."
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<abstract>This paper presents our solution for SemEval-2025 Task 2 on entity-aware machine translation. We propose a parameter-efficient adaptation framework using Low-Rank Adaptation (LoRA) to fine-tune the Qwen2.5-72B model, enabling effective knowledge transfer while preserving generalization capabilities. To address data scarcity and entity ambiguity, we design a Wiki-driven augmentation pipeline that leverages Wikidata’s multilingual entity mappings to generate synthetic training pairs. Our system achieves state-of-the-art performance across 10 languages, securing first place in the competition. Experimental results demonstrate significant improvements in both translation quality (COMET) and entity accuracy (M-ETA).</abstract>
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%0 Conference Proceedings
%T pingan-team at SemEval-2025 Task 2: LoRA-Augmented Qwen2.5 with Wikidata-Driven Entity Translation
%A Chen, Diyang
%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 chen-2025-pingan
%X This paper presents our solution for SemEval-2025 Task 2 on entity-aware machine translation. We propose a parameter-efficient adaptation framework using Low-Rank Adaptation (LoRA) to fine-tune the Qwen2.5-72B model, enabling effective knowledge transfer while preserving generalization capabilities. To address data scarcity and entity ambiguity, we design a Wiki-driven augmentation pipeline that leverages Wikidata’s multilingual entity mappings to generate synthetic training pairs. Our system achieves state-of-the-art performance across 10 languages, securing first place in the competition. Experimental results demonstrate significant improvements in both translation quality (COMET) and entity accuracy (M-ETA).
%U https://aclanthology.org/2025.semeval-1.268/
%P 2065-2070
Markdown (Informal)
[pingan-team at SemEval-2025 Task 2: LoRA-Augmented Qwen2.5 with Wikidata-Driven Entity Translation](https://aclanthology.org/2025.semeval-1.268/) (Chen, SemEval 2025)
ACL