Neural Network Transduction Models in Transliteration Generation

Abstract

In this paper we examine the effectiveness of neural network sequence-to-sequence transduction in the task of transliteration generation. In this year's shared evaluation we submitted two systems into all tasks. The primary system was based on the system used for the NEWS 2012 workshop, but was augmented with an additional feature which was the generation probability from a neural network. The secondary system was the neural network model used on its own together with a simple beam search algorithm. Our results show that adding the neural network score as a feature into the phrase-based statistical machine transliteration system was able to increase the performance of the system. In addition, although the neural network alone was not able to match the performance of our primary system (which exploits it), it was able to deliver a respectable performance for most language pairs which is very promising considering the recency of this technique. c 2015 Association for Computational Linguistics.