Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A8SAHFFQU" target="_blank" >RIV/00216208:11320/22:8SAHFFQU - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2022.findings-naacl.87" target="_blank" >https://aclanthology.org/2022.findings-naacl.87</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/2022.findings-naacl.87" target="_blank" >10.18653/v1/2022.findings-naacl.87</a>
Alternative languages
Result language
angličtina
Original language name
Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks
Original language description
Code-switching dependency parsing stands as a challenging task due to both the scarcity of necessary resources and the structural difficulties embedded in code-switched languages. In this study, we introduce novel sequence labeling models to be used as auxiliary tasks for dependency parsing of code-switched text in a semi-supervised scheme. We show that using auxiliary tasks enhances the performance of an LSTM-based dependency parsing model and leads to better results compared to an XLM-R-based model with significantly less computational and time complexity. As the first study that focuses on multiple code-switching language pairs for dependency parsing, we acquire state-of-the-art scores on all of the studied languages. Our best models outperform the previous work by 7.4 LAS points on average.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Findings of the Association for Computational Linguistics: NAACL 2022
ISBN
978-1-955917-76-6
ISSN
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e-ISSN
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Number of pages
13
Pages from-to
1159-1171
Publisher name
Association for Computational Linguistics
Place of publication
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Event location
Seattle, United States
Event date
Jan 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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