One-shot Learning for Question-Answering in Gaokao History Challenge
Table 5: Accuracy for labeling task.
['Method', '1-shot', '2-shot', '5-shot', '10-shot']
All the baseline methods are fed the training instances as the same shot style of NTM and parameters are tuned for best performance. The results show that NTM works effectively on a small amount of samples. With memory cells as well, LSTM is chosen as the baseline. The hidden layer dimension of the LSTM is 200, which is the same as the NTM controller size. The LSTMs are fed with the training instances as the same shot style of NTM. The effectiveness of NTM may attribute to its capability of slowly learning the representations of raw data through weight updates, and rapidly binding relevant information after a single representation via an external memory. To that extend, the NTM could generalize the meta features of each question type and distinguish the perspectives. This indicates one-shot learning is able to lessen over-fitting issues from sparse features of limited data.