Leveraging Pre-Trained Embeddings for Welsh Taggers

Ignatius Ezeani, Scott Piao, Steven Neale, Paul Rayson, Dawn Knight

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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.
Anthology ID:
W19-4332
Volume:
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Isabelle Augenstein, Spandana Gella, Sebastian Ruder, Katharina Kann, Burcu Can, Johannes Welbl, Alexis Conneau, Xiang Ren, Marek Rei
Venue:
RepL4NLP
SIG:
SIGREP
Publisher:
Association for Computational Linguistics
Note:
Pages:
270–280
Language:
URL:
https://aclanthology.org/W19-4332
DOI:
10.18653/v1/W19-4332
Bibkey:
Cite (ACL):
Ignatius Ezeani, Scott Piao, Steven Neale, Paul Rayson, and Dawn Knight. 2019. Leveraging Pre-Trained Embeddings for Welsh Taggers. In Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019), pages 270–280, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Leveraging Pre-Trained Embeddings for Welsh Taggers (Ezeani et al., RepL4NLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W19-4332.pdf