Translation Quality Estimation using Recurrent Neural Network
Raj Nath Patel, M. Sasikumar · 2016
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.