计算机科学与探索2026，Vol.20Issue(9)：2504-2519,16.DOI:10.3778/j.issn.1673-9418.2509005
面向低资源机器翻译的知识蒸馏方法研究综述
Review of Research on Knowledge Distillation Methods for Low-Resource Machine Translation
摘要
Abstract
With the advancement of deep learning,neural machine translation models have achieved remarkable progress in translation tasks for high-resource language pairs and reached high levels on multiple automatic evaluation metrics.Low-resource machine translation,however,is often constrained by the scarcity of parallel corpora and insufficient linguistic research support,leading to a tendency for models to overfit and consequently making it difficult to improve translation quality.As an efficient model compression and knowledge transfer technique,knowledge distillation can transfer the knowledge of a teacher model to a student model,effectively enhancing the generalization and robustness of the student model in low-resource scenarios.This study presents a systematic review of knowledge distillation methods in the field of low-resource machine translation.Firstly,it elaborates on the concept and development history of knowledge distillation,sorts out its research progress thread from high-resource to low-resource scenarios,and introduces relevant evaluation metrics and datasets.Then,from three dimensions,distillation content,training methods,and model architectures,it analyzes in detail the principles,processes,advantages,disadvantages,and applicable scenarios of various knowledge distillation methods,and verifies their practical effectiveness through typical cases.Furthermore,it points the core challenges faced by low-resource machine translation in terms of data scarcity,corpus noise,insufficient utilization of monolingual resources and catastrophic forgetting,and systematically elaborates on the corresponding solutions of knowledge distillation technology to address the above challenges.Finally,it provides an outlook on future research directions,including robust distillation mechanisms,synergy between multi-modal and cross-domain distillation,self-distillation and teacher-free distillation,and joint optimization of data,models and distillation.This study provides a theoretical framework and development path for low-resource translation tasks,and offers a reference for the in-depth development of multilingual and multimodal intelligent translation research.关键词
知识蒸馏/低资源机器翻译/模型压缩/数据稀缺Key words
knowledge distillation/low-resource machine translation/model compression/data scarcity分类
信息技术与安全科学引用本文复制引用
高雨思,王斯日古楞,斯琴图..面向低资源机器翻译的知识蒸馏方法研究综述[J].计算机科学与探索,2026,20(9):2504-2519,16.基金项目
内蒙古自然科学基金重点项目(2026ZD037) （2026ZD037）
内蒙古自治区科技计划项目(2025YFDZ0010) （2025YFDZ0010）
内蒙古自然科学基金(2024LHMS06024). This work was supported by the Key Program of the Natural Science Foundation of Inner Mongolia(2026ZD037),the Science and Technology Plan Project of Inner Mongolia Autonomous Region(2025YFDZ0010),and the Natural Science Foundation of Inner Mon-golia(2024LHMS06024). （2024LHMS06024）
