Climbing the Tower of Treebanks: Improving Low-Resource Dependency Parsing via Hierarchical Source Selection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10441731" target="_blank" >RIV/00216208:11320/21:10441731 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Climbing the Tower of Treebanks: Improving Low-Resource Dependency Parsing via Hierarchical Source Selection

Original language description

Recent work on multilingual dependency parsing focused on developing highly multilingual parsers that can be applied to a wide range of low-resource languages. In this work, we substantially outperform such &quot;one model to rule them all&quot; approach with a heuristic selection of languages and treebanks on which to train the parser for a specific target language. Our approach, dubbed TOWER, first hierarchically clusters all Universal Dependencies languages based on their mutual syntactic similarity computed from human-coded URIEL vectors. For each low-resource target language, we then climb this language hierarchy starting from the leaf node of that language and heuristically choose the hierarchy level at which to collect training treebanks. This treebank selection heuristic is based on: (i) the aggregate size of all treebanks subsumed by the hierarchy level and (ii) the similarity of the languages in the training sample with the target language. For languages without development treebanks, we additionally use (ii) for model selection (i.e., early stopping) in order to prevent overfitting to development treebanks of closest languages. Our TOWER approach shows substantial gains for low-resource languages over two state-of-the-art multilingual parsers, with more than 20 LAS point gains for some of those languages. Parsing models and code available at: https://github.com/codogogo/towerparse.

Czech name

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Czech description

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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)

Project

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Continuities

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Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

ISBN

978-1-954085-54-1

ISSN

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e-ISSN

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Number of pages

11

Pages from-to

4878-4888

Publisher name

Association for Computational Linguistics

Place of publication

Stroudsburg

Event location

online

Event date

Aug 1, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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