Lexical Normalization and POS-tagging for Code-switched Data
Table 3: Word level accuracies for language identification (10-fold).
['[EMPTY]', 'Indonesian-English MarMoT', 'Indonesian-English Bilty', 'Indonesian-English MaChAmp', 'Turkish-German MarMoT', 'Turkish-German Bilty', 'Turkish-German MaChAmp']
Unsurprisingly, the performances are in line with the chronological order of the introduction of the systems, and their computational complexity. It should be noted that for MaChAmp we used pre-trained embeddings which were trained on the largest amount of external data. The hardest class is ‘Unspecified’; even though this class contains quite some easy cases (punctuation), there are also many harder cases, where a word belongs to any language other than Lang1 and Lang2, or when the annotator is uncertain. From the single label results, we can see that Bilty is more balanced compared to MarMoT, and that MaChAmp consistently outperforms the other two models. \newcitebarik -etal-2019-normalization use a conditional random fields classifier with a variety of features for this task, and report 90.11 accuracy for the Id-En dataset in a 5-fold cross-validation setting.