We present novel features designed with a deep neural network for Machine Translation (MT) Quality Estimation (QE). The features are learned with a Continuous Space Language Model to estimate the probabilities of the source and target segments. These new features, along with standard MT system-independent features, are benchmarked on a series of datasets with various quality labels, including postediting effort, human translation edit rate, post-editing time and METEOR. Results show significant improvements in prediction over the baseline, as well as over systems trained on state of the art feature sets for all datasets. More notably, the addition of the newly proposed features improves over the best QE systems in WMT12 and WMT14 by a significant margin.
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