Visualizing and Understanding Neural Models in NLP

Understand

While neural networks have been successfully applied to many NLP tasks the resulting vector-based models are very difficult to interpret.

• For example it's not clear how they achieve {\em compositionality}, building sentence meaning from the meanings of words and phrases.
• In this paper we describe four strategies for visualizing compositionality in neural models for NLP, inspired by similar work in computer vision.
• We first plot unit values to visualize compositionality of negation, intensification, and concessive clauses, allow us to see well-known markedness asymmetries in negation.