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We propose a novel neural network model for joint part-of-speech (POS)tagging and dependency parsing. Our model extends the well-known BISTgraph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporatinga BiLSTM-based tagging component to produce automatically predicted POS tagsfor the parser. On the benchmark English Penn treebank, our model obtainsstrong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+%absolute improvements to the BIST graph-based parser, and also obtaining astate-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimentalresults on parsing 61 "big" Universal Dependencies treebanks from raw textsshow that our model outperforms the baseline UDPipe (Straka and Strakov\'a,2017) with 0.8% higher average POS tagging score and 3.6% higher average LASscore. In addition, with our model, we also obtain state-of-the-art downstreamtask scores for biomedical event extraction and opinion analysis applications.Our code is available together with all pre-trained models at:https://github.com/datquocnguyen/jPTDP

An improved neural network model for joint POS tagging and dependency parsing