Papers by Sanath Jayasena
BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification (2022.lrec-1)
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| Challenge: | Large-scale monolingual pre-trained language models have shown promising results for high-resource as well as lowresource languages, especially for text classification. |
| Approach: | They provide a set of recommendations for using pre-trained models for Sinhala text classification and introduce new annotated datasets useful for future research. |
| Outcome: | The proposed models are far superior to existing models for Sinhala and set a strong baseline for text classification when fine-tuned. |
Improving domain-specific SMT for low-resourced languages using data from different domains (L18-1)
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| Challenge: | Evaluation of domain-specific statistical machine translation system for official government letters . use of pseudo in-domain data showed improvement for both test sets . |
| Approach: | They develop a statistical machine translation system for official government letters . the system is based on a parallel in-domain dataset containing official letters based in Sinhala and Tamil . |
| Outcome: | The proposed system improves on the in-domain data in the domain of official government letters . the evaluations show that the system requires quality data from diverse subject matters and sources to perform better. |