Ezeani, I. orcid.org/0000-0001-8286-9997, Piao, S., Neale, S. et al. (2 more authors) (2019) Leveraging pre-trained embeddings for Welsh taggers. In: Augenstein, I., Gella, S., Ruder, S., Kann, K., Can, B., Welbl, J., Conneau, A., Ren, X. and Rei, M., (eds.) Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019). 4th Workshop on Representation Learning for NLP (RepL4NLP-2019), 02 Aug 2019, Florence, Italy. Association for Computational Linguistics (ACL) , pp. 270-280.
Abstract
While the application of word embedding models to downstream Natural Language Processing (NLP) tasks has been shown to be successful, the benefits for low-resource languages is somewhat limited due to lack of adequate data for training the models. However, NLP research efforts for low-resource languages have focused on constantly seeking ways to harness pre-trained models to improve the performance of NLP systems built to process these languages without the need to re-invent the wheel. One such language is Welsh and therefore, in this paper, we present the results of our experiments on learning a simple multi-task neural network model for part-of-speech and semantic tagging for Welsh using a pre-trained embedding model from FastText. Our model’s performance was compared with those of the existing rule-based stand-alone taggers for part-of-speech and semantic taggers. Despite its simplicity and capacity to perform both tasks simultaneously, our tagger compared very well with the existing taggers.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2019 Association for Computational Linguistics. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Sep 2019 09:36 |
Last Modified: | 24 Sep 2019 09:36 |
Status: | Published |
Publisher: | Association for Computational Linguistics (ACL) |
Refereed: | Yes |
Identification Number: | 10.18653/v1/w19-4332 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151193 |