Papers by Hour Kaing
PrahokBART: A Pre-trained Sequence-to-Sequence Model for Khmer Natural Language Generation (2025.coling-main)
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| Challenge: | Pre-trained sequence-to-sequence models are typically pretrained on extensive raw text corpora and fine-tuned on task-specific data. |
| Approach: | They introduce a pre-trained sequence-to-sequence model trained from scratch for Khmer using carefully curated Khmer and English corpora. |
| Outcome: | The proposed model outperforms existing models on three generative tasks and is data-efficient and effective in enhancing performance across various natural language generation tasks. |
Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English (D19-52)
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Benjamin Marie, Hour Kaing, Aye Myat Mon, Chenchen Ding, Atsushi Fujita, Masao Utiyama, Eiichiro Sumita
| Challenge: | Using cleaned and normalized noisy monolingual data, supervised neural and statistical machine translation systems performed among the best for the four translation directions. |
| Approach: | They present supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks. |
| Outcome: | The proposed systems performed among the best for the four translation directions. |
A Myanmar (Burmese)-English Named Entity Transliteration Dictionary (2020.lrec-1)
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| Challenge: | Currently, there are no data available for the transcription of borrowed English words in Myanmar . lack of resources is a problem for many understudied languages . |
| Approach: | They construct a dictionary of Myanmar-English transliteration instances using a CC BY-NC-SA license. |
| Outcome: | The proposed model outperforms the statistical model significantly on the character level. |
Robust Neural Machine Translation for Abugidas by Glyph Perturbation (2024.eacl-short)
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| Challenge: | Neural machine translation systems are vulnerable when trained on limited data. |
| Approach: | They propose to add noise to the training phase to increase robustness of NMT systems trained on limited data. |
| Outcome: | The proposed training strategy overcomes noise and improves robustness for low-resource tasks for abugida glyphs. |