Attention Link: An Efficient Attention-Based Low Resource Machine Translation Architecture
This addresses the challenge of machine translation for low-resource languages, though it appears incremental as it builds on existing transformer methods.
The paper tackles the problem of transformer-based machine translation requiring large bilingual corpora by proposing an attention link architecture for low-resource settings, achieving a new state-of-the-art BLEU score of 37.9 on the IWSLT14 de-en task.
Transformers have achieved great success in machine translation, but transformer-based NMT models often require millions of bilingual parallel corpus for training. In this paper, we propose a novel architecture named as attention link (AL) to help improve transformer models' performance, especially in low training resources. We theoretically demonstrate the superiority of our attention link architecture in low training resources. Besides, we have done a large number of experiments, including en-de, de-en, en-fr, en-it, it-en, en-ro translation tasks on the IWSLT14 dataset as well as real low resources scene on bn-gu and gu-ta translation tasks on the CVIT PIB dataset. All the experiment results show our attention link is powerful and can lead to a significant improvement. In addition, we achieve a 37.9 BLEU score, a new sota, on the IWSLT14 de-en task by combining our attention link and other advanced methods.