Third, we develop Goodness, a novel framework to predict word and sentence level of machine translation confidence with dependency structures. The framework allows MT systems to inform users which words are likely translated correctly and how confident it is about the whole sentence. Experimental results show that the MT error prediction accuracy is increased from 69.1 to 72.2 in F-score. The Pearson correlation between the proposed confidence measure and the human-targeted translation edit rate (HTER) is 0.6. Improvements between 0.4 and 0.9 TER reduction are obtained with the n-best list reranking task using the proposed confidence measure. Also, we present a visualization prototype of MT errors at the word and sentence levels with the objective to improve post-editor productivity. 
