Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation

TL;DR

This paper introduces an adaptive knowledge distillation method that combines multiple high-resource language models to improve low-resource neural machine translation, achieving notable BLEU score gains.

Contribution

It proposes a novel adaptive knowledge distillation technique that dynamically combines multiple teacher models for better low-resource NMT performance.

Findings

Up to +0.9 BLEU score improvement over baselines

Effective transfer from multiple high-resource language pairs

Adaptive weighting enhances distillation quality

Abstract

Scarcity of parallel sentence-pairs poses a significant hurdle for training high-quality Neural Machine Translation (NMT) models in bilingually low-resource scenarios. A standard approach is transfer learning, which involves taking a model trained on a high-resource language-pair and fine-tuning it on the data of the low-resource MT condition of interest. However, it is not clear generally which high-resource language-pair offers the best transfer learning for the target MT setting. Furthermore, different transferred models may have complementary semantic and/or syntactic strengths, hence using only one model may be sub-optimal. In this paper, we tackle this problem using knowledge distillation, where we propose to distill the knowledge of ensemble of teacher models to a single student model. As the quality of these teacher models varies, we propose an effective adaptive knowledge distillation approach to dynamically adjust the contribution of the teacher models during the distillation process. Experiments on transferring from a collection of six language pairs from IWSLT to five low-resource language-pairs from TED Talks demonstrate the effectiveness of our approach, achieving up to +0.9 BLEU score improvement compared to strong baselines.