Bootstrapping the Annotation of UD Learner Treebanks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A8CD8IR7P" target="_blank" >RIV/00216208:11320/25:8CD8IR7P - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198653384&partnerID=40&md5=cccc8dc5beaefe97387ad08737778c1b" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198653384&partnerID=40&md5=cccc8dc5beaefe97387ad08737778c1b</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Bootstrapping the Annotation of UD Learner Treebanks

Original language description

Learner data comes in a variety of formats, making corpora difficult to compare with each other. Universal Dependencies (UD) has therefore been proposed as a replacement for the various ad-hoc annotation schemes. Nowadays, the time-consuming task of building a UD treebank often starts with a round of automatic annotation. The performance of the currently available tools trained on standard language, however, tends to decline substantially upon application to learner text. Grammatical errors play a major role, but a significant performance gap has been observed even between standard test sets and normalized learner essays. In this paper, we investigate how to best bootstrap the annotation of UD learner corpora. In particular, we want to establish whether Target Hypotheses (THs), i.e. grammar-corrected learner sentences, are suitable training data for fine-tuning a parser aimed for original (ungrammatical) L2 material. We perform experiments using English and Italian data from two of the already available UD learner corpora. Our results show manually annotated THs to be highly beneficial and suggest that even automatically parsed sentences of this kind might be helpful, if available in sufficiently large amounts. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

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

2024

Confidentiality

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

Article name in the collection

Workshop Build. Using Comp. Corpora, BUCC LREC-COLING - Proc.

ISBN

978-249381431-9

ISSN

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e-ISSN

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Number of pages

7

Pages from-to

111-117

Publisher name

European Language Resources Association (ELRA)

Place of publication

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Event location

Torino, Italia

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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