Heterogeneous Encoders Scaling In The Transformer For Neural Machine Translation
This work addresses translation quality enhancement, particularly for low-resource languages, but is incremental as it builds on existing Transformer methods with a novel combination strategy.
The paper tackles the problem of improving neural machine translation by integrating multiple heterogeneous encoders into the Transformer architecture, resulting in a maximum increase of 7.16 BLEU in low-resource languages compared to single-encoder models.
Although the Transformer is currently the best-performing architecture in the homogeneous configuration (self-attention only) in Neural Machine Translation, many State-of-the-Art models in Natural Language Processing are made of a combination of different Deep Learning approaches. However, these models often focus on combining a couple of techniques only and it is unclear why some methods are chosen over others. In this work, we investigate the effectiveness of integrating an increasing number of heterogeneous methods. Based on a simple combination strategy and performance-driven synergy criteria, we designed the Multi-Encoder Transformer, which consists of up to five diverse encoders. Results showcased that our approach can improve the quality of the translation across a variety of languages and dataset sizes and it is particularly effective in low-resource languages where we observed a maximum increase of 7.16 BLEU compared to the single-encoder model.