  Recursive neural networks (RvNN) have been shown useful for learning sentence
representations and helped achieve competitive performance on several natural
language inference tasks. However, recent RvNN-based models fail to learn
simple grammar and meaningful semantics in their intermediate tree
representation. In this work, we propose an attention mechanism over Tree-LSTMs
to learn more meaningful and explainable parse tree structures. We also
demonstrate the superior performance of our proposed model on natural language
inference, semantic relatedness, and sentiment analysis tasks and compare them
with other state-of-the-art RvNN based methods. Further, we present a detailed
qualitative and quantitative analysis of the learned parse trees and show that
the discovered linguistic structures are more explainable, semantically
meaningful, and grammatically correct than recent approaches. The source code
of the paper is available at
https://github.com/atul04/Explainable-Latent-Structures-Using-Attention.
