Joint POS Tagging and Dependency Parsing with Transition-based Neural Networks
Table 4: Final dependency parsing results (LAS) on the UD dataset.
['Method', 'de', 'en', 'es', 'fr', 'it', 'pt', 'sv', 'AVG']
Joint models also achieves significantly better results in comparison with the pipeline models (p-value below 10−5), which is similar to our finding on CTB5.1. Besides, our joint model achieves the best-reported results among the transition-based models, even by using a greedy manner for decoding, which can be attributed to the effective exploration of the interaction between the tagging and parsing in our joint model, while no previous work has studied it under the neural setting to our knowledge. The work of Zhang and Weiss \shortciteZhang:16 resembles our work most, which improve a feed-forward dependency parser by using POS tags in a pipeline way by stack-propagation. While our joint model benefits from the use of LSTM, and in addition, we find that directly using the resulting tags rather than the penultimate hidden representations of a tag classifier leads to better results.