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.
we use Non-Negative Matrix Factorization (NMF) (Lin, 2007): Given a training set with n instances, i.e., X ∈ R v×n , where v is the size of a global vocabulary and the scalar k is the number of topics in the dataset, we learn the topic matrix D ∈ R v×k and context matrix C ∈ R k×n using the following sparse coding algorithm: where each column in C is a sparse representation of an input over all topics and will be used as global context information in our model.