Joint Alignment and Artificial Data Generation: An Empirical Study of Pivot-based Machine Transliteration

In this paper, we first carry out an investi-gation on two existing pivot strategies for statistical machine transliteration, namely system-based and model-based strategies, to figure out the reason why the previous model-based strategy performs much worse than the system-based one. We then propose a joint alignment algorithm to op-timize transliteration alignments jointly across source, pivot and target languages to improve the performance of the model-based strategy. In addition, we further propose a novel synthetic data-based strategy, which artificially generates source-target data using pivot language. Experimental results on benchmarking da-ta show that the proposed joint alignment optimization algorithm significantly im-proves the accuracy of model-based strat-egy and the proposed synthetic data-based strategy is very effective for pivot-based machine transliteration.