Adaptive English Translation Parameter Tuning via Particle Swarm Optimization and Attention Mechanism

Zhigao Wei
Additional contact information
Zhigao Wei: Xinxiang University, China

International Journal of Swarm Intelligence Research (IJSIR), 2025, vol. 16, issue 1, 1-17

Abstract: This paper proposes an English Translation Model Parameter Adaptive Tuning Method Integrating Particle Swarm Optimization (PSO) and Attention Mechanism. We encode key Neural Machine Translation (NMT) hyperparameters as particles and employ a specialized fitness function that rewards both conventional translation metrics and attention alignment quality. Furthermore, a dynamic feedback multiplier modulates PSO velocity updates in real time, incentivizing hyperparameter adjustments when attention scores deviate from predefined thresholds. Experimental evaluations on both IWSLT En–De and WMT En–De datasets demonstrate the effectiveness of this method, yielding improvements in BLEU and ROUGE-L scores relative to standard baselines. Ablation studies further confirm that both the attention alignment term in the fitness function and the dynamic attention feedback are indispensable for achieving superior translation quality and more coherent attention patterns.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSIR.372900 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jsir00:v:16:y:2025:i:1:p:1-17

Access Statistics for this article

International Journal of Swarm Intelligence Research (IJSIR) is currently edited by Yuhui Shi

More articles in International Journal of Swarm Intelligence Research (IJSIR) from IGI Global
Bibliographic data for series maintained by Journal Editor ().