Neural Tree Indexers for Text Understanding
Table 2: Test set performance on answer sentence selection.
['Model', 'MAP', 'MRR']
The classifier with handcrafted features is a SVM model trained with a set of features. The Bigram-CNN model is a simple convolutional neural net. The Deep LSTM and LSTM attention models outperform the previous best result by a large margin, nearly 5-6%. In NASM, they adopt a deep three-layer LSTM and introduced a latent stochastic attention mechanism over the answer sentence. Our NTI model exceeds NASM by approximately 0.4% on MAP for this task.