Bootstrapping Techniques for Polysynthetic Morphological Analysis
Figure 9: Morph-level performance of shallow neural sequence models. Macro P/R/F1 across all morph tags.
['[EMPTY]', 'Precision', 'Recall', 'F1']
Additionally, we measure the performance of the neural models in terms of precision (P), recall (R), and F1 on the morph level: For each morph tag in the gold target test set, we calculate P, R, and F1, and then calculate the macro-average P, R, and F1 across all tags in the test set This method is more granular than all-or-nothing accuracy over the entire translated sequence, and allows us to get a better picture of how the models are doing on the basis of individual tags. All of the neural models outperform the FST in terms of accuracy and coverage However, the FST is more precise, and this may be useful in certain application contexts. The best model is Base+halluc+resample, which improves on the FST by 10.3 percentage points.