Multilingual Language Processing From Bytes
Table 1: Part-of-speech tagging accuracy for two CRF baselines and 2 versions of BTS. CRF+ uses resources external to the training data (word clusters) and CRF uses only the training data. BTS (unlike CRF+ and CRF) is a single model trained on all the languages together, while BTS* is a separate Byte-to-Span model for each language.
['[BOLD] Language', '[BOLD] CRF+', '[BOLD] CRF', '[BOLD] BTS', '[BOLD] BTS*']
The single BTS model improves on average over the CRF models trained using the same data, though clearly there is some benefit in using external resources. Note that BTS is particularly strong in Finnish, surpassing even CRF+ by nearly 1.5% (absolute), probably because the byte representation generalizes better to agglutinative languages than word-based models, a finding validated by Ling et al. \shortciteling2015finding. In addition, the baseline CRF models, including the (compressed) cluster tables, require about 50 MB per language, while BTS is under 10 MB. BTS improves on average over BTS*, suggesting that it is learning some language-independent representation.