Learning Text Pair Similarity with Context-sensitive Autoencoders

Abstract

We present a pairwise context-sensitive Autoencoder for computing text pair similarity. Our model encodes input text into context-sensitive representations and uses them to compute similarity between text pairs. Our model outperforms the state-of-the-art models in two semantic retrieval tasks and a contextual word similarity task. For retrieval, our unsupervised approach that merely ranks inputs with respect to the cosine similarity between their hidden representations shows comparable performance with the state-of-the-art supervised models and in some cases outperforms them.