Snigdha Singhania


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Statistical Machine Transliteration Baselines for NEWS 2018
Snigdha Singhania | Minh Nguyen | Gia H. Ngo | Nancy Chen
Proceedings of the Seventh Named Entities Workshop

This paper reports the results of our trans-literation 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.