A Monolingual Approach to Contextualized Word Embeddingsfor Mid-Resource Languages
Table 5: Scores from UDPipe 2.0 (from Kondratyuk and Straka, 2019), the previous state-of-the-art models UDPipe 2.0+mBERT (Straka et al., 2019) and UDify (Kondratyuk and Straka, 2019), and our ELMo-enhanced UDPipe 2.0 models. Test scores are given for UPOS, UAS and LAS in all five languages. Best scores are shown in bold, second best scores are underlined.
['Treebank', 'Model', 'UPOS', 'UAS', 'LAS']
We obtain the state of the art for the three metrics in each of the languages with the UDPipe 2.0 + ELMoOSCAR models. We also see that in every single case the UDPipe 2.0 + ELMoOSCAR result surpasses the UDPipe 2.0 + ELMoWikipedia one, suggesting that the size of the pre-training data plays an important role in downstream task results. This is also supports our hypothesis that the OSCAR corpora, being multi-domain, exhibits a better coverage of the different styles, genres and uses present at least in these 5 languages.