Revisiting Checkpoint Averaging for Neural Machine Translation
This work addresses the empirical selection of checkpoints in neural machine translation, but it is incremental as it shows limited gains beyond standard practices.
The paper revisited checkpoint averaging for neural machine translation, confirming its necessity for optimal performance but finding that extensions like weighted averaging or gradient-based methods yield only marginal improvements over simple averaging.
Checkpoint averaging is a simple and effective method to boost the performance of converged neural machine translation models. The calculation is cheap to perform and the fact that the translation improvement almost comes for free, makes it widely adopted in neural machine translation research. Despite the popularity, the method itself simply takes the mean of the model parameters from several checkpoints, the selection of which is mostly based on empirical recipes without many justifications. In this work, we revisit the concept of checkpoint averaging and consider several extensions. Specifically, we experiment with ideas such as using different checkpoint selection strategies, calculating weighted average instead of simple mean, making use of gradient information and fine-tuning the interpolation weights on development data. Our results confirm the necessity of applying checkpoint averaging for optimal performance, but also suggest that the landscape between the converged checkpoints is rather flat and not much further improvement compared to simple averaging is to be obtained.