Improved Sentence Modeling using Suffix Bidirectional LSTM
Table 5: Accuracy of SuBiLSTM and BiLSTM on the QUORA test set with a Siamese base model. All previous results use attention mechanisms.
['Model', 'Test']
Note that unlike the BCN and ESIM models, we use a simple Siamese architecture without any attention mechanism. In fact, the representation of a sentence in this case is simply the max-pooling of all the intermediate representations of the SuBiLSTM. Even in this case, we observe gains over both single layer and 2-layer BiLSTMs, although slightly lesser than the attention based models. The best model (SuBiLSTM) achieves 88.2%, at par with a more complex attention based model BiMPM