Google Research

82 Treebanks, 34 Models: Universal Dependency Parsing with Cross-Treebank Models

  • Aaron Smith
  • Bernd Bohnet
  • Joakim Nivre
  • Miryam de Lhoneux
  • Sara Stymne
  • Yan Shao
Conference on Computational Natural Language Learning (2018) (to appear)

Abstract

We present the Uppsala system for the CoNLL 2018 Shared Task on Multilingual Parsing from Raw Text to Universal Dependencies. Our system is a pipeline consisting of three components: the first performs joint word and sentence segmentation; the second predicts part-of speech tags and morphological features; the third predicts dependency trees from words and tags. Instead of training a single parsing model for each treebank, we trained models with multiple treebanks for the same language or closely related languages, greatly reducing the number of models. On the official test run, we achieved a macro-averaged LAS F1 of 72.37 and a macro-averaged MLAS F1 of 59.20, ranking 7th of 27 teams for both of these metrics.

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