Universal dependency parsing with a general transition-based DAG parser

Daniel Hershcovich; Omri Abend; Ari Rappoport

doi:10.18653/v1/K18-2010

Universal dependency parsing with a general transition-based DAG parser

Daniel Hershcovich
, Omri Abend
, Ari Rappoport

The Hebrew University of Jerusalem

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task
Subtitle of host publication	Multilingual Parsing from Raw Text to Universal Dependencies
Publisher	Association for Computational Linguistics (ACL)
Pages	103-112
Number of pages	10
ISBN (Electronic)	9781948087827
DOIs	https://doi.org/10.18653/v1/K18-2010
State	Published - 2018
Event	2018 SIGNLL Conference on Computational Natural Language Learning, CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, CoNLL 2018 - Brussels, Belgium Duration: 31 Oct 2018 → 1 Nov 2018

Publication series

Name	CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

Conference

Conference	2018 SIGNLL Conference on Computational Natural Language Learning, CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, CoNLL 2018
Country/Territory	Belgium
City	Brussels
Period	31/10/18 → 1/11/18

ASJC Scopus subject areas

Linguistics and Language
Artificial Intelligence
Human-Computer Interaction

Access to Document

10.18653/v1/K18-2010

Cite this

Hershcovich, D., Abend, O., & Rappoport, A. (2018). Universal dependency parsing with a general transition-based DAG parser. 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. 103-112). (CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/K18-2010

Hershcovich, Daniel ; Abend, Omri ; Rappoport, Ari. / Universal dependency parsing with a general transition-based DAG parser. CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Association for Computational Linguistics (ACL), 2018. pp. 103-112 (CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies).

@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 = "English",

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)",

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booktitle = "CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task",

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Hershcovich, D, Abend, O & Rappoport, A 2018, Universal dependency parsing with a general transition-based DAG parser. 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. CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, Association for Computational Linguistics (ACL), pp. 103-112, 2018 SIGNLL Conference on Computational Natural Language Learning, CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, CoNLL 2018, Brussels, Belgium, 31/10/18. https://doi.org/10.18653/v1/K18-2010

Universal dependency parsing with a general transition-based DAG parser. / Hershcovich, Daniel; Abend, Omri ; Rappoport, Ari.
CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Association for Computational Linguistics (ACL), 2018. p. 103-112 (CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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BT - CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task

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Hershcovich D, Abend O , Rappoport A. Universal dependency parsing with a general transition-based DAG parser. 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. Association for Computational Linguistics (ACL). 2018. p. 103-112. (CoNLL 2018 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies). doi: 10.18653/v1/K18-2010

Universal dependency parsing with a general transition-based DAG parser

Abstract

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ASJC Scopus subject areas

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