Multi-Cast Attention Networks for Retrieval-based Question Answering and Response Prediction
Table 1. Performance Comparison on Ubuntu Dialogue Corpus. Best result is in boldface and second best is underlined.
['[EMPTY]', '[ITALIC] R2@1', '[ITALIC] R10@1', '[ITALIC] R10@2', '[ITALIC] R10@5']
Clearly, we observe that all MCAN models achieve a huge performance gain over existing state-of-the-art models. More specifically, the improvement across all metrics are ≈5%−9% better than KEHNN. The performance improvement over strong baselines such as AP-LSTM and MV-LSTM are even greater, hitting an improvement of 15% in terms of R10@1. This ascertains the effectiveness of the MCAN model. Overall, MCAN (FM) and MCAN (NN) are comparable in terms of performance. MCAN (SM) is marginally lower than both MCAN (FM) and MCAN (NN). However, its performance is still considerably higher than the existing state-of-the-art models.