Morphological and Language-Agnostic Word Segmentation for NMT This work has been supported by the grants 18-24210S of the Czech Science Foundation, SVV 260 453 and “Progress” Q18+Q48 of Charles University, H2020-ICT-2014-1-645452 (QT21) of the EU, and using language resources distributed by the LINDAT/CLARIN project of the Ministry of Education, Youth and Sports of the Czech Republic (projects LM2015071 and OP VVV VI CZ.02.1.01/0.0/0.0/16 013/0001781). We thank Jaroslava Hlaváčová for digitizing excerpts of [slavickova1975] used as gold-standard data for evaluating the segmentation methods.
Table 3: BPE vs STE with/without underscore after every (non-final) token of a sentence and/or shared vocabulary. Reported scores are avg±stddev of T2T checkpoints between 275k and 325k training steps. CharacTER, chrF3 and BEER are multiplied by 100.
['[BOLD] split', '[BOLD] underscore', '[BOLD] shared vocab', '[BOLD] BLEU', '[BOLD] CharacTER', '[BOLD] chrF3', '[BOLD] BEER']
It turns out that STE performs almost 5(!) BLEU point better than the default BPE. The underscore feature allowing to model zero suffix almost closes the gap and shared vocabulary also helps a little.