Syntax-based Attention Model for Natural Language Inference
Table 2: Results of our proposed models against other neural models on SNLI corpus. Hidden. is the number of neurons in hidden state h. Train, Dev. and Test denote the classification accuracy. SAT-LSTMs denote our proposed syntax-based attention model. dLSTM and cLSTM represent LSTMs are built over a dependency and constituency respectively.
['Model', 'Hidden.', 'Train acc. (%)', 'Dev. acc. (%)', 'Test acc. (%)']
From the table, we can observe that: For two kinds of syntax-based LSTM encoders, cLSTM achieve better performances than dLSTM, which is consistent with \newcitegildea2004dependencies experiment results on tree-based alignment. We think the reason is that constituency-based model can better learn the semantic compositionality and it has taken the orders of child nodes into consideration. Irrespective of attention mechanism, both two syntax-based LSTM encoders are superior to sequence-based LSTM encoder, which indicates the effectiveness of syntax-based composition. SAT-cLSTMs surpass all the competitor methods and achieve the best performance. More precisely, SAT-cLSTMs outperform AT-LSTMs by 2.1%, and are superior to Tree-LSTM encoders by 3.8%, which suggests the importance of incorporating syntactic information into attention models.