Cross-lingual Semantic Role Labelling with the Valpal database knowledge

Research output: Chapter in Book or Conference Publication/ProceedingConference Publicationpeer-review

1 Citation (Scopus)

Abstract

Cross-lingual Transfer Learning typically involves training a model on a high-resource source language and applying it to a low-resource target language. In this work we introduce a lexical database called Valency Patterns Leipzig (ValPal) which provides the argument pattern information about various verb-forms in multiple languages including low-resource languages. We also provide a framework to integrate the ValPal database knowledge into the state-of-the-art LSTM based model for cross-lingual semantic role labelling. Experimental results show that integrating such knowledge resulted in am improvement in performance of the model on all the target languages on which it is evaluated.

Original languageEnglish
Title of host publicationDeeLIO 2022 - Deep Learning Inside Out
Subtitle of host publication3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, Proceedings of the Workshop
EditorsEneko Agirre, Marianna Apidianaki, Ivan Vulic
PublisherAssociation for Computational Linguistics (ACL)
Pages1-10
Number of pages10
ISBN (Electronic)9781955917322
Publication statusPublished - 2022
EventDeep Learning Inside Out: 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, DeeLIO 2022 - Virtual, Dublin, Ireland
Duration: 27 May 2022 → …

Publication series

NameDeeLIO 2022 - Deep Learning Inside Out: 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, Proceedings of the Workshop

Conference

ConferenceDeep Learning Inside Out: 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, DeeLIO 2022
Country/TerritoryIreland
CityVirtual, Dublin
Period27/05/22 → …

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