Growing Together: Modeling Human Language Learning With n-Best Multi-Checkpoint Machine Translation
Table 4: Performance on the STAPLE 2020 Train and Dev data splits.
['[EMPTY]', '[BOLD] Train Data  [BOLD] Basic Model', '[BOLD] Train Data  [BOLD] Basic Model', '[BOLD] Train Data  [BOLD] Basic Model', '[BOLD] Train Data  [BOLD] Basic Model', '[BOLD] Train Data  [BOLD] Extended Model', '[BOLD] Train Data  [BOLD] Extended Model', '[BOLD] Train Data  [BOLD] Extended Model', '[BOLD] Train Data  [BOLD] Fine-Tuned Model', '[BOLD] Train Data  [BOLD] Fine-Tuned Model', '[BOLD] Train Data  [BOLD] Fine-Tuned Model']
Evaluation on DEV. For evaluation on the STAPLE development data, we adopt the same procedure followed for evaluation on the train split. Discussion. As expected, fine-tuning the En→Pt basic model with the S-TRAIN data-set improves the results with a mean of +1.46% on the training data. We also observe that training on the concatenated OPUS and S-TRAIN data-sets from scratch leads to better results compared to the exclusive fine-tuning method.