Recurrent Neural Network Encoder with Attention for Community Question Answering
Table 3: cQA task results with augmented features (bold is best).
['Model', 'Task A MAP', 'Task A F1', 'Task B MAP', 'Task B F1', 'Task C MAP', 'Task C F1']
To further enhance the system, we incorporate a one hot vector of the original IR ranking as an additional feature into the FNN classifier. In comparing the models with and without augmented features, we can see large improvement for task B and C. The F1 score for task A degrades slightly but MAP improves. This might be because task A already had a substantial amount of training data.