Cross-attention Is All You Need: Adapting Pretrained Transformers For Machine Translation

Mozhdeh Gheini, Xiang Ren, Jonathan May. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing 2021 – 49 citations

[Paper]    
Model Architecture Attention Mechanism Transformer Fine-Tuning RAG Reinforcement Learning Training Techniques

We study the power of cross-attention in the Transformer architecture within the context of transfer learning for machine translation, and extend the findings of studies into cross-attention when training from scratch. We conduct a series of experiments through fine-tuning a translation model on data where either the source or target language has changed. These experiments reveal that fine-tuning only the cross-attention parameters is nearly as effective as fine-tuning all parameters (i.e., the entire translation model). We provide insights into why this is the case and observe that limiting fine-tuning in this manner yields cross-lingually aligned embeddings. The implications of this finding for researchers and practitioners include a mitigation of catastrophic forgetting, the potential for zero-shot translation, and the ability to extend machine translation models to several new language pairs with reduced parameter storage overhead.

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