@inproceedings{7722f2a252064fa6a7408aa7ef197b9c,
title = "Universal dependency parsing with a general transition-based DAG parser",
abstract = "This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task. TUPA is a general neural transition-based DAG parser, which we use to present the first experiments on recovering enhanced dependencies as part of the general parsing task. TUPA was designed for parsing UCCA, a cross-linguistic semantic annotation scheme, exhibiting reentrancy, discontinuity and non-terminal nodes. By converting UD trees and graphs to a UCCA-like DAG format, we train TUPA almost without modification on the UD parsing task. The generic nature of our approach lends itself naturally to multitask learning. Our code is available at https://github.com/CoNLL-UD-2018/HUJI.",
author = "Daniel Hershcovich and Omri Abend and Ari Rappoport",
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/K18-2010",
language = "الإنجليزيّة",
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 = "103--112",
booktitle = "CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task",
address = "الولايات المتّحدة",
}