Joint POS Tagging and Dependency Parsing with Transition-based Neural Networks
Table 3: Final results on the datasets of PTB and CTB5.1, where the tagging accuracy being 100% denotes gold-standard POS tags are employed. We include the results of state-of-the-art previous transition-based parsers as well. In particular, Andor et al. (2016) use beam search in decoding and Bohnet and Nivre (2012) use a different method to produce dependency trees.
['Method', 'PTB POS', 'PTB UAS', 'PTB LAS', 'CTB5 POS', 'CTB5 UAS', 'CTB5 LAS']
We include the pipeline performances as well. Our joint model brings significant improvements on both POS tagging (POS) and dependency parsing (LAS) compared with the pipeline model (the p-value is below 10−5 using pairwise t-test). In addition, we compare our joint model with the baseline parsing model using gold-standard POS tags, which can be treated as the oracle performances of our joint model. Although the joint model gives improved performances over the pipeline model, it still has large spaces to reach the oracle performances, which demonstrates the effectiveness of POS tags in dependency parsing. We compare our model with previous work as well. On the one hand, we compare our joint model with previous joint models. On the other hand, we compare our baseline model with state-of-the-art transition-based dependency parsing models. Typically, the PTB results are reported by using auto POS tags and the CTB5.1 results are reported by using gold-standard POS tags, respectively. Our baseline model produces strong enough results for both PTB and CTB5.1.