@inproceedings{arviv-etal-2020-huji,
title = "{HUJI}-{KU} at {MRP} 2020: Two Transition-based Neural Parsers",
author = "Arviv, Ofir and
Cui, Ruixiang and
Hershcovich, Daniel",
editor = "Oepen, Stephan and
Abend, Omri and
Abzianidze, Lasha and
Bos, Johan and
Haji{\v{c}}, Jan and
Hershcovich, Daniel and
Li, Bin and
O'Gorman, Tim and
Xue, Nianwen and
Zeman, Daniel",
booktitle = "Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.conll-shared.7/",
doi = "10.18653/v1/2020.conll-shared.7",
pages = "73--82",
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."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="arviv-etal-2020-huji">
<titleInfo>
<title>HUJI-KU at MRP 2020: Two Transition-based Neural Parsers</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ofir</namePart>
<namePart type="family">Arviv</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruixiang</namePart>
<namePart type="family">Cui</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniel</namePart>
<namePart type="family">Hershcovich</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Stephan</namePart>
<namePart type="family">Oepen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Omri</namePart>
<namePart type="family">Abend</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lasha</namePart>
<namePart type="family">Abzianidze</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Johan</namePart>
<namePart type="family">Bos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jan</namePart>
<namePart type="family">Hajič</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniel</namePart>
<namePart type="family">Hershcovich</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bin</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tim</namePart>
<namePart type="family">O’Gorman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nianwen</namePart>
<namePart type="family">Xue</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniel</namePart>
<namePart type="family">Zeman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">arviv-etal-2020-huji</identifier>
<identifier type="doi">10.18653/v1/2020.conll-shared.7</identifier>
<location>
<url>https://aclanthology.org/2020.conll-shared.7/</url>
</location>
<part>
<date>2020-11</date>
<extent unit="page">
<start>73</start>
<end>82</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T HUJI-KU at MRP 2020: Two Transition-based Neural Parsers
%A Arviv, Ofir
%A Cui, Ruixiang
%A Hershcovich, Daniel
%Y Oepen, Stephan
%Y Abend, Omri
%Y Abzianidze, Lasha
%Y Bos, Johan
%Y Hajič, Jan
%Y Hershcovich, Daniel
%Y Li, Bin
%Y O’Gorman, Tim
%Y Xue, Nianwen
%Y Zeman, Daniel
%S Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F arviv-etal-2020-huji
%X 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.
%R 10.18653/v1/2020.conll-shared.7
%U https://aclanthology.org/2020.conll-shared.7/
%U https://doi.org/10.18653/v1/2020.conll-shared.7
%P 73-82
Markdown (Informal)
[HUJI-KU at MRP 2020: Two Transition-based Neural Parsers](https://aclanthology.org/2020.conll-shared.7/) (Arviv et al., CoNLL 2020)
ACL