  This paper describes Stanford's system at the CoNLL 2018 UD Shared Task. We
introduce a complete neural pipeline system that takes raw text as input, and
performs all tasks required by the shared task, ranging from tokenization and
sentence segmentation, to POS tagging and dependency parsing. Our single system
submission achieved very competitive performance on big treebanks. Moreover,
after fixing an unfortunate bug, our corrected system would have placed the
2nd, 1st, and 3rd on the official evaluation metrics LAS,MLAS, and BLEX, and
would have outperformed all submission systems on low-resource treebank
categories on all metrics by a large margin. We further show the effectiveness
of different model components through extensive ablation studies.
