Attention Link: An Efficient Attention-Based Low Resource Machine Translation Architecture
Zeping Min · Procedia Computer Science · 2023
Transformers have emerged as a pivotal tool in machine translation. Nonetheless, their effectiveness typically hinges on extensive training with millions of bilingual parallel corpora. This paper presents a novel architecture, termed as Attention Link (AL), which is specifically designed to bolster the performance of transformer models in situations where training resources are scarce. We furnish theoretical substantiation that underscores the superiority of the AL architecture in such resource-limited settings. Experimental outcomes lend further credence to the enhancements brought about by our methodology, marking it as a stride towards enhancing the performance of transformer models.