Hierarchical Memory Networks for Answer Selection on Unknown Words
Table 2: Comparison of predicted test error numbers of our HMN and MemNN with different components on four domain datasets (Test: 6,000 samples) and the Total dataset (Test: 24,000 samples). MemNN-H1: Memory network with 1 hop and temporal encoding, MemNN-NT: Memory network with 3 hops but without temporal encoding. MemNN: Memory network with 3 hops and temporal encoding. Note that HMN-Sent and HMN-Word, as the parts of HMN-Joint, are joint learning but give their predicted answers separately.
['[EMPTY]', '[ITALIC] Air-Ticket (CH)', '[ITALIC] Hotel (CH)', '[ITALIC] Air-Ticket (EN)', '[ITALIC] Hotel (EN)', '[ITALIC] Total']
In order to evaluate the effect of multiple hops for reasoning module and temporal encoding on sentence-level memory, we design three MemNN We further evaluate the prediction performance of our HMN on different level memory components via HMN-Sent (prediction of reasoning module on sentence-level memory as Eqn. and HMN-Joint (joint prediction as Eqn. From the results, we can see that MemNN-H1 without temporal encoding and MemNN-NT without multi-hop reasoning make the worst performances, which clearly demonstrate that multiple hops for reasoning module and temporal encoding on sentence-level memory play a very important role on our tasks. Despite that MemNN represents surprising results on this task, our HMN-Word and HMN-joint further improve the answer selection performance on all the datasets. Compared with the results of HMN-Sent and HMN-Word, the results also show that the joint prediction can make a better performance. As the effect of temporal encoding as Eqn. An explanation maybe that the way in which these methods use a simple way, rather than sophisticated LSTM or GRU, to encoding the sentence into memory as Eqn. This simple sentence encoding strategy can remain the raw embedding representation of words, and MemNN and HMN-Sent utilize the transpose of the raw embedding matrix as the decoding weights to conduct answer match as Eqn. in the raw embedding space. Nonetheless, sentence-level encoding may introduce other semantic information which may lead to predict an error answer, as the prediction (Sent) in the example.