CLUSE: Cross-Lingual Unsupervised Sense Embeddings
Table 3: Accuracy on cross-lingual document classification (%).
['[BOLD] Model', '[BOLD] EN2DE', '[BOLD] DE2EN']
We can see that our proposed model achieves comparable performance or even superior performance to most prior work on the DE2EN direction; however, the same conclusion does not hold for the EN2DE direction. The reason may be that we test the model that works best on BCWS and hence not able to tune hyperparameters on the development set of CLDC. In addition, we use the average of sense vectors as input word embeddings, which may induce some noises into the resulting vectors. In sum, the comparable performance of the downstream task shows the practical usage and the potential extension of the proposed model.