  This paper describes our submission to the shared task on word/phrase level
Quality Estimation (QE) in the First Conference on Statistical Machine
Translation (WMT16). The objective of the shared task was to predict if the
given word/phrase is a correct/incorrect (OK/BAD) translation in the given
sentence. In this paper, we propose a novel approach for word level Quality
Estimation using Recurrent Neural Network Language Model (RNN-LM) architecture.
RNN-LMs have been found very effective in different Natural Language Processing
(NLP) applications. RNN-LM is mainly used for vector space language modeling
for different NLP problems. For this task, we modify the architecture of
RNN-LM. The modified system predicts a label (OK/BAD) in the slot rather than
predicting the word. The input to the system is a word sequence, similar to the
standard RNN-LM. The approach is language independent and requires only the
translated text for QE. To estimate the phrase level quality, we use the output
of the word level QE system.
