Thazin Myint Oo

Also published as: Thazin Myint Oo


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NECTEC’s Participation in WAT-2021
Zar Zar Hlaing | Ye Kyaw Thu | Thazin Myint Oo | Mya Ei San | Sasiporn Usanavasin | Ponrudee Netisopakul | Thepchai Supnithi
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

In this paper, we report the experimental results of Machine Translation models conducted by a NECTEC team for the translation tasks of WAT-2021. Basically, our models are based on neural methods for both directions of English-Myanmar and Myanmar-English language pairs. Most of the existing Neural Machine Translation (NMT) models mainly focus on the conversion of sequential data and do not directly use syntactic information. However, we conduct multi-source neural machine translation (NMT) models using the multilingual corpora such as string data corpus, tree data corpus, or POS-tagged data corpus. The multi-source translation is an approach to exploit multiple inputs (e.g. in two different formats) to increase translation accuracy. The RNN-based encoder-decoder model with attention mechanism and transformer architectures have been carried out for our experiment. The experimental results showed that the proposed models of RNN-based architecture outperform the baseline model for English-to-Myanmar translation task, and the multi-source and shared-multi-source transformer models yield better translation results than the baseline.

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Hybrid Statistical Machine Translation for English-Myanmar: UTYCC Submission to WAT-2021
Ye Kyaw Thu | Thazin Myint Oo | Hlaing Myat Nwe | Khaing Zar Mon | Nang Aeindray Kyaw | Naing Linn Phyo | Nann Hwan Khun | Hnin Aye Thant
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

In this paper we describe our submissions to WAT-2021 (Nakazawa et al., 2021) for English-to-Myanmar language (Burmese) task. Our team, ID: “YCC-MT1”, focused on bringing transliteration knowledge to the decoder without changing the model. We manually extracted the transliteration word/phrase pairs from the ALT corpus and applying XML markup feature of Moses decoder (i.e. -xml-input exclusive, -xml-input inclusive). We demonstrate that hybrid translation technique can significantly improve (around 6 BLEU scores) the baseline of three well-known “Phrase-based SMT”, “Operation Sequence Model” and “Hierarchical Phrase-based SMT”. Moreover, this simple hybrid method achieved the second highest results among the submitted MT systems for English-to-Myanmar WAT2021 translation share task according to BLEU (Papineni et al., 2002) and AMFM scores (Banchs et al., 2015).


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Statistical Machine Translation between Myanmar (Burmese) and Dawei (Tavoyan)
Thazin Myint Oo | Ye Kyaw Thu | Khin Mar Soe | Thepchai Supnithi
Proceedings of the First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers

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Neural Machine Translation between Myanmar (Burmese) and Rakhine (Arakanese)
Thazin Myint Oo | Ye Kyaw Thu | Khin Mar Soe
Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects

This work explores neural machine translation between Myanmar (Burmese) and Rakhine (Arakanese). Rakhine is a language closely related to Myanmar, often considered a dialect. We implemented three prominent neural machine translation (NMT) systems: recurrent neural networks (RNN), transformer, and convolutional neural networks (CNN). The systems were evaluated on a Myanmar-Rakhine parallel text corpus developed by us. In addition, two types of word segmentation schemes for word embeddings were studied: Word-BPE and Syllable-BPE segmentation. Our experimental results clearly show that the highest quality NMT and statistical machine translation (SMT) performances are obtained with Syllable-BPE segmentation for both types of translations. If we focus on NMT, we find that the transformer with Word-BPE segmentation outperforms CNN and RNN for both Myanmar-Rakhine and Rakhine-Myanmar translation. However, CNN with Syllable-BPE segmentation obtains a higher score than the RNN and transformer.


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UCSYNLP-Lab Machine Translation Systems for WAT 2018
Yi Mon Shwe Sin | Thazin Myint Oo | Hsu Myat Mo | Win Pa Pa | Khim Mar Soe | Ye Kyaw Thu
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation