HUJI-KU at MRP 2020: Two Transition-based Neural Parsers

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

HUJI-KU at MRP 2020 : Two Transition-based Neural Parsers. / Arviv, Ofir; Cui, Ruixiang; Hershcovich, Daniel.

Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing. Association for Computational Linguistics, 2020. s. 73-82.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Arviv, O, Cui, R & Hershcovich, D 2020, HUJI-KU at MRP 2020: Two Transition-based Neural Parsers. i Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing. Association for Computational Linguistics, s. 73-82, CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing, 19/11/2020. https://doi.org/10.18653/v1/2020.conll-shared.7

APA

Arviv, O., Cui, R., & Hershcovich, D. (2020). HUJI-KU at MRP 2020: Two Transition-based Neural Parsers. I Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing (s. 73-82). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.conll-shared.7

Vancouver

Arviv O, Cui R, Hershcovich D. HUJI-KU at MRP 2020: Two Transition-based Neural Parsers. I Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing. Association for Computational Linguistics. 2020. s. 73-82 https://doi.org/10.18653/v1/2020.conll-shared.7

Author

Arviv, Ofir ; Cui, Ruixiang ; Hershcovich, Daniel. / HUJI-KU at MRP 2020 : Two Transition-based Neural Parsers. Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing. Association for Computational Linguistics, 2020. s. 73-82

Bibtex

@inproceedings{4dca01904bb54833ab13e862f479e3c1,
title = "HUJI-KU at MRP 2020: Two Transition-based Neural Parsers",
abstract = "This paper describes the HUJI-KU system submission to the shared task on CrossFramework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system in the 2019 MRP shared task. Both are transition-based parsers using BERT contextualized embeddings. We generalized TUPA to support the newly-added MRP frameworks and languages, and experimented with multitask learning with the HIT-SCIR parser. We reached 4th place in both the crossframework and cross-lingual tracks.",
author = "Ofir Arviv and Ruixiang Cui and Daniel Hershcovich",
year = "2020",
doi = "10.18653/v1/2020.conll-shared.7",
language = "English",
pages = "73--82",
booktitle = "Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing",
publisher = "Association for Computational Linguistics",
note = "CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing, ; Conference date: 19-11-2020 Through 20-11-2020",

}

RIS

TY - GEN

T1 - HUJI-KU at MRP 2020

T2 - CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing,

AU - Arviv, Ofir

AU - Cui, Ruixiang

AU - Hershcovich, Daniel

PY - 2020

Y1 - 2020

N2 - This paper describes the HUJI-KU system submission to the shared task on CrossFramework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system in the 2019 MRP shared task. Both are transition-based parsers using BERT contextualized embeddings. We generalized TUPA to support the newly-added MRP frameworks and languages, and experimented with multitask learning with the HIT-SCIR parser. We reached 4th place in both the crossframework and cross-lingual tracks.

AB - This paper describes the HUJI-KU system submission to the shared task on CrossFramework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system in the 2019 MRP shared task. Both are transition-based parsers using BERT contextualized embeddings. We generalized TUPA to support the newly-added MRP frameworks and languages, and experimented with multitask learning with the HIT-SCIR parser. We reached 4th place in both the crossframework and cross-lingual tracks.

U2 - 10.18653/v1/2020.conll-shared.7

DO - 10.18653/v1/2020.conll-shared.7

M3 - Article in proceedings

SP - 73

EP - 82

BT - Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing

PB - Association for Computational Linguistics

Y2 - 19 November 2020 through 20 November 2020

ER -

ID: 254670439