Gated Group Self-Attention for Answer Selection
Table 5. Results on insuranceQA. The results of models marked with ⋆ are reported from (Tran and Niedereée, 2018).
['Model', 'Test1', 'Test2']
Results on insuranceQA We compare GGSA and iGGSA with baselines on insuranceQA dataset. Note that the result of our implementation for Multihop-Sequential-LSTM (our impl.) is better than the result from the original paper. This is due to some details, such as pretrained word embedding and hyperparameter tuning, are different. Besides max-pooling and attention, we also adopt multihop sequential attention as composition module for iGGSA, which is named iGGSA with multihop-seq-att. We can see that it achieves the state-of-art result on this dataset, outperforming the counterpart with LSTM as an encoder. The results verify the effectiveness of our GGSA and iGGSA.