DBMS-KU Interpolation for WMT19 News Translation Task

Sari Dewi Budiwati, Al Hafiz Akbar Maulana Siagian, Tirana Noor Fatyanosa, Masayoshi Aritsugi

Abstract
This paper presents the participation of DBMS-KU Interpolation system in WMT19 shared task, namely, Kazakh-English language pair. We examine the use of interpolation method using a different language model order. Our Interpolation system combines a direct translation with Russian as a pivot language. We use 3-gram and 5-gram language model orders to perform the language translation in this work. To reduce noise in the pivot translation process, we prune the phrase table of source-pivot and pivot-target. Our experimental results show that our Interpolation system outperforms the Baseline in terms of BLEU-cased score by +0.5 and +0.1 points in Kazakh-English and English-Kazakh, respectively. In particular, using the 5-gram language model order in our system could obtain better BLEU-cased score than utilizing the 3-gram one. Interestingly, we found that by employing the Interpolation system could reduce the perplexity score of English-Kazakh when using 3-gram language model order.
Anthology ID:
W19-5309
Volume:
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
Month:
August
Year:
2019
Address:
Florence, Italy
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
141–146
Language:
URL:
https://aclanthology.org/W19-5309
DOI:
10.18653/v1/W19-5309
Bibkey:
Cite (ACL):
Sari Dewi Budiwati, Al Hafiz Akbar Maulana Siagian, Tirana Noor Fatyanosa, and Masayoshi Aritsugi. 2019. DBMS-KU Interpolation for WMT19 News Translation Task. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 141–146, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
DBMS-KU Interpolation for WMT19 News Translation Task (Budiwati et al., WMT 2019)
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https://preview.aclanthology.org/auto-file-uploads/W19-5309.pdf
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