Full-Time Supervision based Bidirectional RNN for Factoid Question Answering
Table 3: Effect of full-time supervision and output layer.
['Model', 'Configuration', 'Literature InnerP', 'Literature LR', 'History InnerP', 'History LR']
4.3.1 Effect of Full-Time Supervision and Output Layer Because FTS-BRNN always needs the output layer to make the dimensionality of answers and questions be equal , we demonstrate the effect of full-time supervision and output layer based on FTS-BRNN-s. " has-output" represents the model with an output layer o(t)=Wo⋅f(t)+Uo⋅b(t)+bias while "no-output" represents the model without an output layer by directly using concatenating operation to get the hidden states h(t)=[f(t),b(t)]. We can find that the FTS-BRNN-s with full-time supervision can dramatically outperform the counterpart with pooling loss, which successfully verifies the effectiveness of full-time supervision. Furthermore, we can also find that the output layer in FTS-BRNN and FTS-BRNN-s is also very important. In addition, FTS-BRNN is slightly better than FTS-BRNN-s for this factoid QA task.