Neural Probabilistic Model for Non-projective MST Parsing
Table 2: Parsing performance (UAS and LAS) of different versions of our model on both the development and test sets for three languages.
['[EMPTY]', '[BOLD] English  [BOLD] Dev', '[BOLD] English  [BOLD] Dev', '[BOLD] English  [BOLD] Test', '[BOLD] English  [BOLD] Test', '[BOLD] Chinese  [BOLD] Dev', '[BOLD] Chinese  [BOLD] Dev', '[BOLD] Chinese  [BOLD] Test', '[BOLD] Chinese  [BOLD] Test', '[BOLD] German  [BOLD] Dev', '[BOLD] German  [BOLD] Dev', '[BOLD] German  [BOLD] Test', '[BOLD] German  [BOLD] Test']
We first construct experiments to dissect the effectiveness of each input information (embeddings) of our neural network architecture by ablation studies. We compare the performance of four versions of our model with different inputs — Basic, +POS, +Char and Full — where the Basic model utilizes only the pretrained word embeddings as inputs, while the +POS and + Char models augments the basic one with POS embedding and character information, respectively. Char model obtains better performance than the Basic model on all the three languages, showing that character-level representations are important for dependency parsing. Second, on English and German, +Char and +POS achieves comparable performance, while on Chinese +POS significantly outperforms +Char model. Finally, the Full model achieves the best accuracy on English and German, but on Chinese +POS obtains the best. Thus, we guess that the POS information is more useful for Chinese than English and German.