Supervised mover’s distance: A simple model for sentence comparison

Abstract

We propose a simple neural network model which can learn relation between sentences by passing their representations obtained from Long Short Term Memory (LSTM) through a Relation Network. The Relation Network module tries to extract similarity between multiple contextual representations obtained from LSTM. The aim is to build a model which is simple to implement, light in terms of parameters and works across multiple supervised sentence comparison tasks. We show good results for the model on two sentence comparison datasets.