Abstract
This paper reports the results of our transliteration experiments conducted on NEWS 2018 Shared Task dataset. We focus on creating the baseline systems trained using two open-source, statistical transliteration tools, namely Sequitur and Moses. We discuss the pre-processing steps performed on this dataset for both the systems. We also provide a re-ranking system which uses top hypotheses from Sequitur and Moses to create a consolidated list of transliterations. The results obtained from each of these models can be used to present a good starting point for the participating teams.
Cite
CITATION STYLE
Singhania, S., Nguyen, M., Ngo, H. G., & Chen, N. F. (2018). Statistical Machine Transliteration Baselines for NEWS 2018. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 74–78). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w18-2410
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