@inproceedings{d292a7925f564c9b947f530c56ec01ee,
title = "Towards better UD parsing: Deep contextualized word embeddings, ensemble, and treebank concatenation",
abstract = "This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We base our submission on Stanford's winning system for the CoNLL 2017 shared task and make two effective extensions: 1) incorporating deep contextualized word embeddings into both the part of speech tagger and dependency parser; 2) ensembling parsers trained with different initialization. We also explore different ways of concatenating treebanks for further improvements. Experimental results on the development data show the effectiveness of our methods. In the final evaluation, our system was ranked first according to LAS (75.84\%) and outperformed the other systems by a large margin.",
author = "Wanxiang Che and Yijia Liu and Yuxuan Wang and Bo Zheng and Ting Liu",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computational Linguistics; 2018 SIGNLL Conference on Computational Natural Language Learning, CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, CoNLL 2018 ; Conference date: 31-10-2018 Through 01-11-2018",
year = "2018",
doi = "10.18653/v1/K18-2005",
language = "英语",
series = "CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
publisher = "Association for Computational Linguistics (ACL)",
pages = "55--64",
booktitle = "CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task",
address = "澳大利亚",
}