Neural Tree Indexers for Text Understanding
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']
For comparison, we include the results from the published state-of-the-art systems. 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; which present strong baseline systems. Our best score on this task is 87.3% accuracy obtained with the full tree matching NTI model. The previous best performing model on the task performs phrase matching by using the attention mechanism.