Multitask Learning with Deep Neural Networks for Community Question Answering
Table 2: Impact of CNN vs. LSTM sentence models on the baseline network for Task C.
['Model', 'MAP', 'MRR']
The sentence encoder, xsi=f(si), outputs a fixed-size embedding of the input sentence si. The sentence vectors, xsi, are concatenated together and given in input to a Multi-Layer Perceptron, which is constituted by a non-linear hidden layer and an sigmoid output layer. We observe that the two sentence models show comparable results. For the rest of the experiments, we used the CNN sentence model, since it shows faster convergence rate and more stable results with respect to the LSTM sentence model. In particular, we noted that there is an improvement of 3 points in MRR.