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        <title>Data Augmentation Method for Boosting Multilingual Word Sense Disambiguation</title>
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        <namePart type="given">Arkadiusz</namePart>
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    <name type="personal">
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            <namePart type="family">Rigau</namePart>
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        <name type="personal">
            <namePart type="given">Francis</namePart>
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            <namePart type="given">Alexandre</namePart>
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    <abstract>Recent advances in Word Sense Disambiguation suggest neural language models can be successfully improved by incorporating knowledge base structure. Such class of models are called hybrid solutions. We propose a method of improving hybrid WSD models by harnessing data augmentation techniques and bilingual training. The data augmentation consist of structure augmentation using interlingual connections between wordnets and text data augmentation based on multilingual glosses and usage examples. We utilise language-agnostic neural model trained both with SemCor and Princeton WordNet gloss and example corpora, as well as with Polish WordNet glosses and usage examples. This augmentation technique proves to make well-known hybrid WSD architecture to be competitive, when compared to current State-of-the-Art models, even more complex.</abstract>
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%0 Conference Proceedings
%T Data Augmentation Method for Boosting Multilingual Word Sense Disambiguation
%A Janz, Arkadiusz
%A Maziarz, Marek
%Y Rigau, German
%Y Bond, Francis
%Y Rademaker, Alexandre
%S Proceedings of the 12th Global Wordnet Conference
%D 2023
%8 January
%I Global Wordnet Association
%C University of the Basque Country, Donostia - San Sebastian, Basque Country
%F janz-maziarz-2023-data
%X Recent advances in Word Sense Disambiguation suggest neural language models can be successfully improved by incorporating knowledge base structure. Such class of models are called hybrid solutions. We propose a method of improving hybrid WSD models by harnessing data augmentation techniques and bilingual training. The data augmentation consist of structure augmentation using interlingual connections between wordnets and text data augmentation based on multilingual glosses and usage examples. We utilise language-agnostic neural model trained both with SemCor and Princeton WordNet gloss and example corpora, as well as with Polish WordNet glosses and usage examples. This augmentation technique proves to make well-known hybrid WSD architecture to be competitive, when compared to current State-of-the-Art models, even more complex.
%U https://aclanthology.org/2023.gwc-1.7
%P 60-66

Markdown (Informal)

[Data Augmentation Method for Boosting Multilingual Word Sense Disambiguation](https://aclanthology.org/2023.gwc-1.7) (Janz & Maziarz, GWC 2023)

• Data Augmentation Method for Boosting Multilingual Word Sense Disambiguation (Janz & Maziarz, GWC 2023)

ACL

• Arkadiusz Janz and Marek Maziarz. 2023. Data Augmentation Method for Boosting Multilingual Word Sense Disambiguation. In Proceedings of the 12th Global Wordnet Conference, pages 60–66, University of the Basque Country, Donostia - San Sebastian, Basque Country. Global Wordnet Association.