Embedding Projection for Targeted Cross-Lingual Sentiment:Model Comparisons and a Real-World Study
Table 13: Macro F1 of targeted cross-lingual models on Twitter data in 10 target languages. Twitter refers to models that have been trained on the English data mentioned in Table 10, while USAGE and SemEval are trained on the English data from the datasets mentioned in Section 4.1.2.
['Binary', 'Training Data', 'Model', 'EU', 'CA', 'GL', 'IT', 'FR', 'NL', 'DE', 'NO', 'SV', 'DA', 'avg.']
The final column is the average over all languages. Given the results from the earlier experiments, we hypothesize that MT should outperform Muse, VecMap and Blse for most of the languages.