Neural Semantic Encoders
Table 1: Training and test accuracy on natural language inference task. d is the word embedding size and |θ|M the number of model parameters.
['Model', '[ITALIC] d', '| [ITALIC] θ| [ITALIC] M', 'Train', 'Test']
The NSE model encodes each sentence simultaneously by using a separate memory for each sentence. The second model - MMA-NSE first encodes the premise and then the hypothesis sentence by sharing the premise encoded memory in addition to the hypothesis memory. For the third model, we use inter-sentence attention which selectively reconstructs the premise representation. The classifier with handcrafted features extracts a set of lexical features. The next group of models are based on sentence encoding. While most of the sentence encoder models rely solely on word embeddings, the dependency tree CNN and the SPINN-PI models make use of sentence parser output. The SPINN-PI model is similar to NSE in spirit that it also explicitly computes word composition. However, the composition in the SPINN-PI is guided by supervisions from a dependency parser. NSE outperformed the previous sentence encoders on this task. The MMA-SNE further slightly improved the result, indicating that reading the premise memory is helpful while encoding the hypothesis.