Hour Kaing


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Multi-Source Cross-Lingual Constituency Parsing
Hour Kaing | Chenchen Ding | Katsuhito Sudoh | Masao Utiyama | Eiichiro Sumita | Satoshi Nakamura
Proceedings of the 18th International Conference on Natural Language Processing (ICON)

Pretrained multilingual language models have become a key part of cross-lingual transfer for many natural language processing tasks, even those without bilingual information. This work further investigates the cross-lingual transfer ability of these models for constituency parsing and focuses on multi-source transfer. Addressing structure and label set diversity problems, we propose the integration of typological features into the parsing model and treebank normalization. We trained the model on eight languages with diverse structures and use transfer parsing for an additional six low-resource languages. The experimental results show that the treebank normalization is essential for cross-lingual transfer performance and the typological features introduce further improvement. As a result, our approach improves the baseline F1 of multi-source transfer by 5 on average.


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A Myanmar (Burmese)-English Named Entity Transliteration Dictionary
Aye Myat Mon | Chenchen Ding | Hour Kaing | Khin Mar Soe | Masao Utiyama | Eiichiro Sumita
Proceedings of the Twelfth Language Resources and Evaluation Conference

Transliteration is generally a phonetically based transcription across different writing systems. It is a crucial task for various downstream natural language processing applications. For the Myanmar (Burmese) language, robust automatic transliteration for borrowed English words is a challenging task because of the complex Myanmar writing system and the lack of data. In this study, we constructed a Myanmar-English named entity dictionary containing more than eighty thousand transliteration instances. The data have been released under a CC BY-NC-SA license. We evaluated the automatic transliteration performance using statistical and neural network-based approaches based on the prepared data. The neural network model outperformed the statistical model significantly in terms of the BLEU score on the character level. Different units used in the Myanmar script for processing were also compared and discussed.


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Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English
Benjamin Marie | Hour Kaing | Aye Myat Mon | Chenchen Ding | Atsushi Fujita | Masao Utiyama | Eiichiro Sumita
Proceedings of the 6th Workshop on Asian Translation

This paper presents the NICT’s supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks. For all the translation directions, we built state-of-the-art supervised neural (NMT) and statistical (SMT) machine translation systems, using monolingual data cleaned and normalized. Our combination of NMT and SMT performed among the best systems for the four translation directions. We also investigated the feasibility of unsupervised machine translation for low-resource and distant language pairs and confirmed observations of previous work showing that unsupervised MT is still largely unable to deal with them.