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Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Gheini, Mozhdeh
;
May, Jonathan
;
Ren, Xiang
. - : Underline Science Inc., 2021
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.132/ Abstract: 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 ...
Keyword:
Computational Linguistics
;
Machine Learning
;
Machine Learning and Data Mining
;
Machine translation
;
Natural Language Processing
URL:
https://dx.doi.org/10.48448/pgv2-cn55
https://underline.io/lecture/37970-cross-attention-is-all-you-need-adapting-pretrained-transformers-for-machine-translation
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