Gaussian Mixture Latent Vector Grammars
Table 2: Token accuracy (T) and sentence accuracy (S) for POS tagging on the testing data.
['Model', 'WSJ T', 'WSJ S', 'English T', 'English S', 'French T', 'French S', 'German T', 'German S', 'Russian T', 'Russian S', 'Spanish T', 'Spanish S', 'Indonesian T', 'Indonesian S', 'Finnish T', 'Finnish S', 'Italian T', 'Italian S']
It can be seen that, on all the testing data, GM-LVeGs consistently surpass LVGs in terms of both token accuracy and sentence accuracy. GM-LVeG-D is slightly better than GM-LVeG-S in sentence accuracy, producing the best sentence accuracy on 5 of the 9 testing datasets. GM-LVeG-S performs slightly better than GM-LVeG-D in token accuracy on 5 of the 9 datasets. Overall, there is not significant difference between GM-LVeG-D and GM-LVeG-S. However, GM-LVeG-S admits more efficient learning than GM-LVeG-D in practice since it has fewer parameters.