Expanding the Text Classification Toolbox with Cross-Lingual Embeddings
Table 3: TCID 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] bi-GRU-  [BOLD] Att F1', '[ITALIC]  [BOLD] bi-GRU-  [BOLD] Att P', '[ITALIC]  [BOLD] bi-GRU-  [BOLD] Att R', '[ITALIC]  [BOLD] bi-GRU-Att+  [BOLD] Sent-Ali F1', '[ITALIC]  [BOLD] bi-GRU-Att+  [BOLD] Sent-Ali P', '[ITALIC]  [BOLD] bi-GRU-Att+  [BOLD] Sent-Ali R']
The results show that in general multilingual models tend to outperform monolingual baselines for both English and German irrespective of the embeddings model used with an average increase of 7.01%, 6.23% and 4.93% FT-MLP, MF-CNN, and bi-GRU-Att respectively.