@inproceedings{thu-etal-2021-hybrid,
title = "Hybrid Statistical Machine Translation for {E}nglish-{M}yanmar: {UTYCC} Submission to {WAT}-2021",
author = "Thu, Ye Kyaw and
Oo, Thazin Myint and
Nwe, Hlaing Myat and
Mon, Khaing Zar and
Kyaw, Nang Aeindray and
Phyo, Naing Linn and
Khun, Nann Hwan and
Thant, Hnin Aye",
booktitle = "Proceedings of the 8th Workshop on Asian Translation (WAT2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wat-1.7",
doi = "10.18653/v1/2021.wat-1.7",
pages = "83--89",
abstract = "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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<abstract>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).</abstract>
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%0 Conference Proceedings
%T Hybrid Statistical Machine Translation for English-Myanmar: UTYCC Submission to WAT-2021
%A Thu, Ye Kyaw
%A Oo, Thazin Myint
%A Nwe, Hlaing Myat
%A Mon, Khaing Zar
%A Kyaw, Nang Aeindray
%A Phyo, Naing Linn
%A Khun, Nann Hwan
%A Thant, Hnin Aye
%S Proceedings of the 8th Workshop on Asian Translation (WAT2021)
%D 2021
%8 aug
%I Association for Computational Linguistics
%C Online
%F thu-etal-2021-hybrid
%X 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).
%R 10.18653/v1/2021.wat-1.7
%U https://aclanthology.org/2021.wat-1.7
%U https://doi.org/10.18653/v1/2021.wat-1.7
%P 83-89
Markdown (Informal)
[Hybrid Statistical Machine Translation for English-Myanmar: UTYCC Submission to WAT-2021](https://aclanthology.org/2021.wat-1.7) (Thu et al., WAT 2021)
ACL