Probing for Labeled Dependency Trees
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3APQYQ7XZ5" target="_blank" >RIV/00216208:11320/22:PQYQ7XZ5 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2022.acl-long.532" target="_blank" >https://aclanthology.org/2022.acl-long.532</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/2022.acl-long.532" target="_blank" >10.18653/v1/2022.acl-long.532</a>
Alternative languages
Result language
angličtina
Original language name
Probing for Labeled Dependency Trees
Original language description
Probing has become an important tool for analyzing representations in Natural Language Processing (NLP). For graphical NLP tasks such as dependency parsing, linear probes are currently limited to extracting undirected or unlabeled parse trees which do not capture the full task. This work introduces DepProbe, a linear probe which can extract labeled and directed dependency parse trees from embeddings while using fewer parameters and compute than prior methods. Leveraging its full task coverage and lightweight parametrization, we investigate its predictive power for selecting the best transfer language for training a full biaffine attention parser. Across 13 languages, our proposed method identifies the best source treebank 94% of the time, outperforming competitive baselines and prior work. Finally, we analyze the informativeness of task-specific subspaces in contextual embeddings as well as which benefits a full parser's non-linear parametrization provides.
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
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
ISBN
978-1-955917-21-6
ISSN
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e-ISSN
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Number of pages
16
Pages from-to
7711-7726
Publisher name
Association for Computational Linguistics
Place of publication
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Event location
Dublin, Ireland
Event date
Jan 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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