SEntNet: Source-aware Recurrent Entity Networkfor Dialogue Response Selection
Table 1: Comparison with baselines on the dialog bAbI and mDSTC2 datasets. The results are the best turn-level accuracy in 10 runs. Bold results indicate a statistically significant improvement over the strongest baseline (paired t-test, p<0.01).
['[BOLD] Model', '[BOLD] dialog bAbI', '[BOLD] mDSTC2']
Furthermore, the EntNet model significantly outperforms the simple baselines, i.e., TF-IDF and Q2A, by a large margin. Q2A model achieves a surprisingly high turn-level accuracy given the simplicity of the method. Furthermore, the large gap between the performance of SEntNet and the baseline models suggest that non-trivial relations between the dialogue history and responses are found. SEntNet is outperformed by HHCN on the mDSTC2 dataset. This is mainly because HHCN uses a stronger language model (i.e., a word-character RNN) and integrates a NN-based selection for domain knowledge.