Embedding Projection for Targeted Cross-Lingual Sentiment:Model Comparisons and a Real-World Study
Table 4: Macro F1 of four models trained on English and tested on Spanish (ES), Catalan (CA), and Basque (EU). The bold numbers show the best results for each metric per column and the highlighted numbers show where Blse is better than the other projection methods, VecMap, Muse, and Barista (* p < 0.01).
['[EMPTY]', '[EMPTY]', 'Upper Bounds Mono', 'Upper Bounds MT', '[BOLD] BLSE', 'Baselines Unsup', 'Baselines VecMap', 'Baselines Muse', 'Ensemble Barista', 'Ensemble VecMap', 'Ensemble Muse', 'Ensemble Barista', 'Blse']
Our method outperforms the other projection methods (the baselines VecMap, Muse, and Barista) on four of the six experiments substantially. It performs only slightly worse than the more resource-costly upper bounds (MT and Mono). This is especially noticeable for the binary classification task, where Blse performs nearly as well as machine translation and significantly better than the other methods. Unsup also performs similarly to Blse on the binary tasks, while giving stronger performance on the 4-class setup.