Leveraging Pre-Trained Embeddings for Welsh Taggers

Ezeani, Ignatius and Piao, Scott and Neale, Steven and Rayson, Paul and Knight, Dawn (2019) Leveraging Pre-Trained Embeddings for Welsh Taggers. In: Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019). Association for Computational Linguistics, Florence, Italy, pp. 270-280.

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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.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
135950
Deposited By:
Deposited On:
05 Aug 2019 14:05
Refereed?:
Yes
Published?:
Published
Last Modified:
31 Mar 2020 01:09