Improved Sentence Modeling using Suffix Bidirectional LSTM
Table 4: Accuracy of SuBiLSTM and BiLSTM on the SNLI test set with ESIM as the base model.
['Model', 'Test']
Our implementation of ESIM, when using a BiLSTM, achieves 87.8% accuracy. Using a SuBiLSTM, the accuracy jumps to 88.3% and to 88.2% for the Tied version. On using the 2-layer BiLSTM, accuracy improves only marginally by 0.1%. This is aligned with the results shown for text classification above. Here again, the attention mechanism on top of the token representations benefit from the long range contextual information captured by SuBiLSTM. Note that ESIM uses an inter-sentence attention mechanism and is able to exploit the better token representations provided by SuBiLSTM across sentences. We also report the performance of an ensemble of 5 models. Both the SuBiLSTM versions achieve an accuracy of 89.1%, while the BiLSTM based ones perform worse.