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Applying Natural Annotation and Curriculum Learning to Named Entity Recognition for Under-Resourced Languages

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A8VUHD3N4" target="_blank" >RIV/00216208:11320/22:8VUHD3N4 - isvavai.cz</a>

  • Result on the web

    <a href="https://aclanthology.org/2022.coling-1.394" target="_blank" >https://aclanthology.org/2022.coling-1.394</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Applying Natural Annotation and Curriculum Learning to Named Entity Recognition for Under-Resourced Languages

  • Original language description

    Current practices in building new NLP models for low-resourced languages rely either on Machine Translation of training sets from better resourced languages or on cross-lingual transfer from them. Still we can see a considerable performance gap between the models originally trained within better resourced languages and the models transferred from them. In this study we test the possibility of (1) using natural annotation to build synthetic training sets from resources not initially designed for the target downstream task and (2) employing curriculum learning methods to select the most suitable examples from synthetic training sets. We test this hypothesis across seven Slavic languages and across three curriculum learning strategies on Named Entity Recognition as the downstream task. We also test the possibility of fine-tuning the synthetic resources to reflect linguistic properties, such as the grammatical case and gender, both of which are important for the Slavic languages. We demonstrate the possibility to achieve the mean F1 score of 0.78 across the three basic entities types for Belarusian starting from zero resources in comparison to the baseline of 0.63 using the zero-shot transfer from English. For comparison, the English model trained on the original set achieves the mean F1-score of 0.75. The experimental results are available from https://github.com/ValeraLobov/SlavNER

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • Continuities

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 29th International Conference on Computational Linguistics

  • ISBN

  • ISSN

    2951-2093

  • e-ISSN

  • Number of pages

    13

  • Pages from-to

    4468-4480

  • Publisher name

    International Committee on Computational Linguistics

  • Place of publication

  • Event location

    Gyeongju, Republic of Korea

  • Event date

    Jan 1, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article