Abstract

Many effective adaptation techniques for statistical machine translation crucially rely on in-domain development sets to learn model parameters. In this paper we present a novel method that automatically generates the matching tune set for Arabic-to-English MT with limited indomain data1. This technique improves our MT system over two baselines (tuned on data from the same domain but different genres) by 1.2 and 3.5 BLEU points using significantly less tuning data (1/6 and 1/2 of the baselines). Lexical and morphological features contribute to the success of our method in different ways. Generating tune sets using length distribution also improves the system significantly. Finally, our method obtains competitive results in experiments where ingenre tune sets are available.

Date

2012

Authors

Jinying Chen, Jacob Devlin, Huaigu Cao, Rohit Prasad, Prem Natarajan

Conference

Proceedings of the 16th Annual Conference of the European Association for Machine Translation

Pages

161-168