A small Griko-Italian speech translation corpus
Table 3: Boundary scores for the task of unsupervised word segmentation. Results for neural segmentation are the average over 5 runs. Best results for each metric are presented in bold.
['[EMPTY]', '[BOLD] dpseg  [BOLD] P', '[BOLD] dpseg  [BOLD] R', '[BOLD] dpseg  [BOLD] F', '[BOLD] proportional  [BOLD] P', '[BOLD] proportional  [BOLD] R', '[BOLD] proportional  [BOLD] F', '[BOLD] neural  [BOLD] P', '[BOLD] neural  [BOLD] R', '[BOLD] neural  [BOLD] F', '[BOLD] merged neural  [BOLD] P', '[BOLD] merged neural  [BOLD] R', '[BOLD] merged neural  [BOLD] F']
Even on this very low-resource scenario, dpseg has a remarkable performance for the task of word segmentation working with graphemes. It retrieved 75.10% of the correct boundaries (recall). The second best method from the baselines for grapheme segmentation was the merged version of the neural segmentation. The remaining two baselines (proportional and neural) had close performance, achieving retrieval between 44 and 52%.