Leveraging Synthetic Targets for Machine Translation
Sarthak Mittal Oleksii Hrinchuk Oleksii Kuchaiev
In this work, we provide a recipe for training machine translation models in
a limited resource setting by leveraging synthetic target data generated using
a large pre-trained model. We show that consistently across different
benchmarks in bilingual, multilingual, and speech translation setups, training
models on synthetic targets outperforms training on the actual ground-truth
data. This performance gap grows bigger with increasing limits on the amount of
available resources in the form of the size of the dataset and the number of
parameters in the model. We also provide preliminary analysis into whether this
boost in performance is linked to ease of optimization or more deterministic
nature of the predictions, and whether this paradigm leads to better
out-of-distribution performance across different testing domains.