SParse: Ko University Graph-Based Parsing System for the CoNLL 2018 Shared Task
Berkay {\"O}nder, Can G{\"u}meli, Deniz Yuret
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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.