Feb 8, 2019 · In this paper, we optimize Dropconnect by adopting Gaussian approximation in the Bernoulli distribution in low-resource machine translation ...
It is an effective approach to approximate mask calculations to linear operations while being fully trained. An interesting finding is that the adhesive ...
An Optimized Regularization Method to Enhance Low-Resource MT. Y. Ji, H. Hou, Y. Lei, and Z. Ren. PDCAT, volume 931 of Communications in Computer and ...
Regularization factors optimized on one low- resource dataset are beneficial for low-resource datasets in other languages, and benefit from more aggressive ...
Missing: Enhance | Show results with:Enhance
In Part II we show that the use of auxiliary and synthetic data for neural MT, which is another way to perform regularization, also improves quality in low- ...
Missing: Enhance | Show results with:Enhance
An Optimized Regularization Method to Enhance Low-Resource MT: Methods and Applications ... Our approach outperforms the Dropout and Dropconnect for low-resource ...
Jun 5, 2024 · Focusing on improving the translation qualities of a relatively small group of high-resource ... techniques tailored for low-resource languages.
Apr 12, 2024 · Our experiment entails applying Back-translation and Transfer Learning to automatically generate more training data and achieve higher ...
learning, and developed regularization methods to enhance the Transformer model's robustness in zero-shot scenarios. Their approach yielded a 2.23 BLEU ...
We show that in a low-resource setting, a smaller batch size leads to higher scores in a shorter training time, and argue that this is due to better ...