The Effect of Data Partitioning Strategy on Model Generalizability: A Case Study of Morphological Segmentation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AMXI63SC2" target="_blank" >RIV/00216208:11320/25:MXI63SC2 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200208953&partnerID=40&md5=b217c36419987d3221127ab35396708f" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200208953&partnerID=40&md5=b217c36419987d3221127ab35396708f</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

The Effect of Data Partitioning Strategy on Model Generalizability: A Case Study of Morphological Segmentation

Popis výsledku v původním jazyce

Recent work to enhance data partitioning strategies for more realistic model evaluation face challenges in providing a clear optimal choice. This study addresses these challenges, focusing on morphological segmentation and synthesizing limitations related to language diversity, adoption of multiple datasets and splits, and detailed model comparisons. Our study leverages data from 19 languages, including ten indigenous or endangered languages across 10 language families with diverse morphological systems (polysynthetic, fusional, and agglutinative) and different degrees of data availability. We conduct large-scale experimentation with varying sized combinations of training and evaluation sets as well as new test data. Our results show that, when faced with new test data: (1) models trained from random splits are able to achieve higher numerical scores; (2) model rankings derived from random splits tend to generalize more consistently. ©2024 Association for Computational Linguistics.

Název v anglickém jazyce

The Effect of Data Partitioning Strategy on Model Generalizability: A Case Study of Morphological Segmentation

Popis výsledku anglicky

Recent work to enhance data partitioning strategies for more realistic model evaluation face challenges in providing a clear optimal choice. This study addresses these challenges, focusing on morphological segmentation and synthesizing limitations related to language diversity, adoption of multiple datasets and splits, and detailed model comparisons. Our study leverages data from 19 languages, including ten indigenous or endangered languages across 10 language families with diverse morphological systems (polysynthetic, fusional, and agglutinative) and different degrees of data availability. We conduct large-scale experimentation with varying sized combinations of training and evaluation sets as well as new test data. Our results show that, when faced with new test data: (1) models trained from random splits are able to achieve higher numerical scores; (2) model rankings derived from random splits tend to generalize more consistently. ©2024 Association for Computational Linguistics.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proc. Conf. North American Chapter Assoc. Comput. Linguist.: Hum. Lang. Technol., NAACL

ISBN

979-889176114-8

ISSN

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e-ISSN

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Počet stran výsledku

14

Strana od-do

2851-2864

Název nakladatele

Association for Computational Linguistics (ACL)

Místo vydání

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Místo konání akce

Hybrid, Mexico City

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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