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Recently, bidirectional recurrent neural network (BRNN) has been widely usedfor question answering (QA) tasks with promising performance. However, mostexisting BRNN models extract the information of questions and answers bydirectly using a pooling operation to generate the representation for loss orsimilarity calculation. Hence, these existing models don't put supervision(loss or similarity calculation) at every time step, which will lose someuseful information. In this paper, we propose a novel BRNN model calledfull-time supervision based BRNN (FTS-BRNN), which can put supervision at everytime step. Experiments on the factoid QA task show that our FTS-BRNN canoutperform other baselines to achieve the state-of-the-art accuracy.

Full-Time Supervision based Bidirectional RNN for Factoid Question Answering