Expanding the Text Classification Toolbox with Cross-Lingual Embeddings
Table 2: CLDC performance comparison between different text classification architectures highlighting gain per language
['[EMPTY]', '[EMPTY]', '[ITALIC]  [BOLD] FT-  [BOLD] MLP F1', '[ITALIC]  [BOLD] FT-  [BOLD] MLP P', '[ITALIC]  [BOLD] FT-  [BOLD] MLP R', '[ITALIC]  [BOLD] MF-  [BOLD] CNN F1', '[ITALIC]  [BOLD] MF-  [BOLD] CNN P', '[ITALIC]  [BOLD] MF-  [BOLD] CNN R', '[ITALIC]  [BOLD] HAN+  [BOLD] Sent-Ali F1', '[ITALIC]  [BOLD] HAN+  [BOLD] Sent-Ali P', '[ITALIC]  [BOLD] HAN+  [BOLD] Sent-Ali R']
In general, for all languages and all text classification architectures, multilingual training wins over monolingual training with an average improvement in F1-score of 4.47% and 2.52% and 10.55% for FT-MLP and MF-CNN and HAN+Sent-Ali respectively.