We describe the graph-based dependency parser in our system (AntNLP) submitted to the CoNLL 2018 UD Shared Task. We use bidirectional lstm to get the word representation, then a bi-affine pointer networks to compute scores of candidate dependency edges and the MST algorithm to get the final dependency tree. From the official testing results, our system gets 70.90 LAS F1 score (rank 9/26), 55.92 MLAS (10/26) and 60.91 BLEX (8/26).
CITATION STYLE
Ji, T., Liu, Y., Wang, Y., Wu, Y., & Lan, M. (2018). ANTNLP at Conll 2018 shared task: A Graph-based Parser for Universal Dependency Parsing. In CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (pp. 248–255). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/K18-2025
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