A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing
Table 1: Universal POS tagging accuracies and LAS scores computed on all tokens (including punctuation) on test sets for 19 languages in UD v1.2. The language codes with ∙ refer to morphologically rich languages. Numbers (in the second top row) right below language codes are out-of-vocabulary rates. UDPipe is the trainable pipeline for processing CoNLL-U files (Straka et al., 2016). TnT denotes the second order HMM-based TnT tagger (Brants, 2000). CRF denotes the Conditional random fields-based tagger, presented in Plank et al. (2014). BiLSTM-aux refers to the state-of-the-art (SOTA) BiLSTM-based POS tagging model with an additional auxiliary loss for rare words (Plank et al., 2016). Note that the (old) language code for Hebrew “iw” is referred to as “he” as in Plank et al. (2016). [⊕]: Results are reported in Plank et al. (2016). Stack-prop refers to the SOTA Stack-propagation model for joint POS tagging and transition-based dependency parsing (Zhang and Weiss, 2016). ▽-Chars denotes the absolute accuracy decrease of our jPTDP, when the character-based representations of words are not taken into account. B’15 denotes the character-based stack LSTM model for transition-based dependency parsing (Ballesteros et al., 2015). PipelinePtag refers to a greedy version of the approach proposed by Alberti et al. (2015). RBGParser refers to the graph-based dependency parser with tensor decomposition, presented in Lei et al. (2014). [*]: Results are reported in Zhang and Weiss (2016).
['Method', 'ar', 'bg', 'da', 'de∙', 'en', 'es', 'eu∙', 'fa', 'fi∙', 'fr', 'hi', 'id', 'it', 'iw', 'nl', 'no', 'pl∙', 'pt', 'sl∙', 'AVG']
with results reported in prior work, using the same experimental setup. One reason for our better LAS is probably because jPTDP uses character-based representations of words, while Stack-propagation uses feature representations for suffixes and prefixes which might not be as useful as character-based representations for capturing unknown words. So, our jPDTP is particularly good for morphologically rich languages, with 1.7% higher averaged LAS than Stack-propagation over these languages.