  Recursive neural networks (Tree-RNNs) based on dependency trees are
ubiquitous in modeling sentence meanings as they effectively capture semantic
relationships between non-neighborhood words. However, recognizing semantically
dissimilar sentences with the same words and syntax is still a challenge to
Tree-RNNs. This work proposes an improvement to Dependency Tree-RNN (DT-RNN)
using the grammatical relationship type identified in the dependency parse. Our
experiments on semantic relatedness scoring (SRS) and recognizing textual
entailment (RTE) in sentence pairs using SICK (Sentence Involving Compositional
Knowledge) dataset show encouraging results. The model achieved a 2%
improvement in classification accuracy for the RTE task over the DT-RNN model.
The results show that Pearson's and Spearman's correlation measures between the
model's predicted similarity scores and human ratings are higher than those of
standard DT-RNNs.
