Near-Real Time Tactical Automated Machine Translation Technology(N-TAMTT)

Recent DARPA-sponsored MT evaluations showed that phrase-based statistical MT systems derived through automatic training on very large corpora achieve the highest translation quality for both generalized and specialized texts. Intuitively, syntax, morphology, and semantics should help; however, rule-based systems that incorporate such knowledge are not yet able to beat the best statistical systems. We propose the following plan for incorporating richer linguistic knowledge into the translation process:  Start from a good baseline: the best A2E machine translation system in the world (as evaluated by NIST).  Devise experiments to determine major sources of error in the system (E.g.: verb agreement; morphological errors; contextual errors)  Assess the impact that solving these problems may have on translation quality. This is done by measuring the increase in BLEU score when a problem is fixed. Assess how our current system should be enriched to deal with each of these problems.  Rank-list the problems in order of impact on translation quality and feasibility of addressing them using richer linguistic knowledge (morphology, syntax, semantics, context).  Devise solutions for solving these problems in turn and incorporate the solutions into an end-to-end system