SParse: Ko\cc University Graph-Based Parsing System for the CoNLL 2018 Shared Task

We present SParse, our Graph-Based Parsing model submitted for the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (Zeman et al., 2018). Our model extends the state-of-the-art biaffine parser (Dozat and Manning, 2016) with a structural meta-learning module, SMeta, that combines local and global label predictions. Our parser has been trained and run on Universal Dependencies datasets (Nivre et al., 2016, 2018) and has 87.48{\%} LAS, 78.63{\%} MLAS, 78.69{\%} BLEX and 81.76{\%} CLAS (Nivre and Fang, 2017) score on the Italian-ISDT dataset and has 72.78{\%} LAS, 59.10{\%} MLAS, 61.38{\%} BLEX and 61.72{\%} CLAS score on the Japanese-GSD dataset in our official submission. All other corpora are evaluated after the submission deadline, for whom we present our unofficial test results.

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