Enhancing Sentence Relation Modeling with Auxiliary Character-level Embedding
Table 3: Results of Tree LSTM vs Sequence LSTM on auxiliary char embedding.
['Method', 'Accuracy', 'Pearson']
We can see that, if we don’t deploy CNN, simple Tree LSTM yields better result than traditional LSTM, but worse than Bidirectional LSTM. This is reasonable due to the fact that Bidirectional LSTM can enhance sentence representation by concatenating forward and backward representations. We found that adding CNN layer will decrease the accuracy in this scenario. Because when feeding into CNN, we have to reshape the feature planes otherwise convolution will not work. For example, we set convolution kernel width as 2, the input 2D tensor will have the shape lager than 2. To boost performance with CNN, we need more matching features. We found Multi-layer Bidirectional LSTM can incorporate more features and achieve best performance compared with single-layer Bidirectional LSTM.