Improved Transition-Based Parsing and Tagging with Neural Networks

We extend and improve upon recent work in struc-tured training for neural network transition-based dependency parsing. We do this by experimenting with novel features, additional transition systems and by testing on a wider array of languages. In par-ticular, we introduce set-valued features to encode the predicted morphological properties and part-of-speech confusion sets of the words being parsed. We also investigate the use of joint parsing and part-of-speech tagging in the neural paradigm. Finally, we conduct a multi-lingual evaluation that demon-strates the robustness of the overall structured neu-ral approach, as well as the benefits of the exten-sions proposed in this work. Our research further demonstrates the breadth of the applicability of neu-ral network methods to dependency parsing, as well as the ease with which new features can be added to neural parsing models.