Joint Learning of POS and Dependencies for Multilingual Universal Dependency Parsing
This paper describes the system of team LeisureX in the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system predicts the part-of-speech tag and dependency tree jointly. For the basic tasks, including tokenization, lemmatization and morphology prediction, we employ the official baseline model (UDPipe). To train the low-resource languages, we adopt a sampling method based on other richresource languages. Our system achieves a macro-average of 68.31{\%} LAS F1 score, with an improvement of 2.51{\%} compared with the UDPipe.
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Universal Dependencies
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