Full-Time Supervision based Bidirectional RNN for Factoid Question Answering
Table 5: Accuracy comparison to other baselines.
['Model', 'Literature InnerP', 'Literature LR', 'History InnerP', 'History LR']
Because the public scripts of BOW, BOW-DT and QANTA don’t use inner product to choose answers, we don’t report inner product results of these three methods. All the deep methods, including QANTA, QA-LSTM, FTS-BRNN-s and FTS-BRNN, can outperform traditional non-deep methods. Furthermore, all the RNN-based methods, including QA-LSTM, FTS-BRNN-s and FTS-BRNN, can outperform recursive neural network based method (QANTA). In addition, our FTS-BRNN and FTS-BRNN-s can outperform all the other state-of-the-art baselines to achieve the best performance.