Automatic post-editing (APE) systems aim at correcting the output of machine translation systems to produce better quality translations, i.e. produce translations can be manually postedited with an increase in productivity. In this work, we present an APE system that uses statistical models to enhance a commercial rulebased machine translation (RBMT) system. In addition, a procedure for effortless human evaluation has been established. We have tested the APE system with two corpora of different complexity. For the Parliament corpus, we show that the APE system significantly complements and improves the RBMT system. Results for the Protocols corpus, although less conclusive, are promising as well. Finally, several possible sources of errors have been identified which will help develop future system enhancements.

The transcription of historical documents is one of the most interesting tasks in which Handwritten Text Recognition can be applied, due to its interest in humanities research. One alternative for transcribing the ancient manuscripts is the use of speech dictation by using Automatic Speech Recognition techniques. In the two alternatives similar models (Hidden Markov Models and n-grams) and decoding processes (Viterbi decoding) are employed, which allows a possible combination of the two modalities with little difficulties. In this work, we explore the possibility of using recognition results of one modality to restrict the decoding process of the other modality, and apply this process iteratively. Results of these multimodal iterative alternatives are significantly better than the baseline uni-modal systems and better than the non-iterative alternatives.