pingan-team at S em E val-2025 Task 2: L o RA -Augmented Qwen2.5 with W ikidata-Driven Entity Translation (2025)

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).

## introduction the accurate translation of named entities remains a critical challenge in modern machine translation systems, particularly when processing rare, ambiguous, or culture-specific references. this paper presents our approach to task 2 of semeval-2025 @xcite , a shared task aimed at developing robust machine translation systems for complex entity translation between english and 10 target languages: arabic (ar), french (fr), german (de), italian (it), japanese (ja), korean (ko), spanish (es), thai (th), turkish (tr), and traditional chinese (zh-tw). named entities -including personal names, organizations, geographical locations, and culturespecific items (csis) such as movie titles, literary works, and commercial products -present unique translation difficulties. while neural machine translation (nmt) systems have achieved remarkable progress in general domain translation, their performance significantly degrades when encountering low-frequency or domain-specific entities. this limitation stems from multiple factors: the inherent ambiguity of proper nouns across linguistic contexts, the lack of transliteration conventions for emerging entities, and the cultural specificity embedded in certain references. the semeval-2025 task 2 challenge specifically addresses these limitations by constructing a testbed containing carefully curated named entities across three complexity categories: 1) rare entities with limited parallel corpus occurrences 2) crosslingual ambiguous terms 3) novel entities absent from training data. our investigation makes two primary contributions: first, we implement a parameterefficient adaptation framework for the qwen2.5-72b (qwen et al., 2025) model through low-rank adaptation (lora) tuning, enabling effective knowledge transfer while preserving the base model's generalization capabilities. this approach addresses the computational challenges of finetuning ultra-large language models (llms) for domain-specific named entity translation tasks. second, we design a wikidata data-driven augmentation pipeline that systematically injects crosslingual entity knowledge into the training process (see figure 1 ). by leveraging wikidata's multilingual entity mappings and property graphs, our method automatically generates synthetic training pairs. egy and wikidata augmentation pipeline. while traditional nmt systems struggle with low-frequency entities @xcite , our work extends the capabilities of large language models through targeted adaptation, addressing limitations in crosscultural transliteration and contextual disambiguation. ## experimental setup the experiments were conducted on 8×nvidia a100 gpus, which each contain 80gb hbm2 memory. the experiments is evaluated using the official metric of the competition: the harmonic mean of translation quality (comet) and the precision of the translation of the entity (m-eta). comet is a metric for assessing machine translation quality by comparing system outputs to human reference translations. it utilizes a pre-trained model to generate translation quality scores, quantifying the semantic and linguistic fidelity of translations. m-eta evaluates the accuracy of entity translations. given a gold-standard set of entity translations and a system's predicted entities, m-eta calculates the proportion of correctly translated entities. the final overall score will be the harmonic mean of the two scores: ## conclusion our work demonstrates that combining parameterefficient adaptation with structured knowledge injection significantly improves entity translation accuracy. the lora-tuned qwen2.5-72b model outperforms both commercial mt systems and open-source llms. the wikidata augmentation strategy proves particularly effective for handling culture-specific items and rare entities. future work