Reordering rules for English-Hindi SMT
This work addresses translation quality for English-Hindi SMT, but it is incremental as it builds on existing methods with new rules.
The authors tackled the problem of improving English-Hindi statistical machine translation by proposing a rich set of reordering rules as a preprocessing stage, resulting in significant improvements in translation quality as measured by metrics like BLEU and NIST.
Reordering is a preprocessing stage for Statistical Machine Translation (SMT) system where the words of the source sentence are reordered as per the syntax of the target language. We are proposing a rich set of rules for better reordering. The idea is to facilitate the training process by better alignments and parallel phrase extraction for a phrase-based SMT system. Reordering also helps the decoding process and hence improving the machine translation quality. We have observed significant improvements in the translation quality by using our approach over the baseline SMT. We have used BLEU, NIST, multi-reference word error rate, multi-reference position independent error rate for judging the improvements. We have exploited open source SMT toolkit MOSES to develop the system.