Expanding the Text Classification Toolbox with Cross-Lingual Embeddings
Table 1: Training, Validation and Testing Distribution of RCV Dataset across Languages
['[EMPTY]', '[BOLD] English', '[BOLD] German', '[BOLD] French', '[BOLD] Italian']
We follow the same cross-lingual document classification benchmark defined in Klementiev et al. This finding is similar to MF-CNN and confirms our hypothesis that the less resourced a language is, the more likely it is to benefit from multilingual training. Although there is a gain in performance for English, it is marginal for both architectures (only 0.36 at most). Obtaining a monolingual performance for English always on par with multilingual performance is not at all surprising as English is the dominant language accounting for more than 80% of the training and validation data.