Improving interactive machine translation via multiple positive constraints

Since machine translation systems are still unable to produce satisfactory outputs, recently various interactive machine translation (IMT) approaches are proposed. State-of-the-art IMT systems use the human validated prefix as the only constraint that guides decoding, in which the human guidance is quite insufficient. This paper extends the human-computer interactions by allowing translators to provide multiple correct fragments (CFs) besides prefix. These fragments act as positive constraints for decoding. Four improvements are proposed to adapt traditional IMT to this new interaction mode. Experimental results on Chinese-English corpora show that our method achieves lower KSMT scores under comparable prediction speed in comparison with the traditional IMT method.