Boosting English-Chinese Machine Transliteration via High Quality Alignment and Multilingual Resources

Abstract

This paper presents our machine transliteration systems developed for the NEWS 2015 machine transliteration shared task. Our systems are applied to two tasks: English to Chinese and Chinese to English. For standard runs, in which only official data sets are used, we build phrase-based transliteration models with refined alignments provided by the M2M-aligner. For non-standard runs, we add multilingual resources to the systems designed for the standard runs and build different language specific transliteration systems. Linear regression is adopted to rerank the outputs afterwards, which significantly improves the overall transliteration performance.