Multi-Cast Attention Networks for Retrieval-based Question Answering and Response Prediction
Table 3. Performance comparison on QatarLiving dataset for community question answering. Best result is in boldface and second best is underlined.
['Model', 'P@1', 'MAP']
Our best performing MCAN model achieves state-of-the-art performance on this dataset. Performance improvement over recent, competitive neural network baselines is significant. Notably, the improvement of MCAN (FM) over AI-CNN on the P@1 metric is 4.1% and 1.1% in terms of MAP. MCAN (FM) also achieves competitive results relative to the CTRN model. The performance of MCAN (NN) and MCAN (SM) is lower than MCAN (FM) but still remains competitive on this benchmark.