A Rule-Augmented Statistical Phrase-based Translation System
Cong Duy Vu Hoang, Ai Ti Aw, Nhung T. H. Nguyen · 2014
Interactive or Incremental Statistical Machine Translation (IMT) aims to provide a mechanism that allows the statistical models involved in the translation process to be incrementally updated and improved.The source of knowledge normally comes from users who either post-edit the entire translation or just provide the translations for wrongly translated domain-specific terminologies.Most of the existing work on IMT uses batch learning paradigm which does not allow translation systems to make use of the new input instantaneously.We introduce an adaptive MT framework with a Rule Definition Language (RDL) for users to amend MT results through translation rules or patterns.Experimental results show that our system acknowledges user feedback via RDL which improves the translations of the baseline system on three test sets for Vietnamese to English translation.