Unsupervised semantic association learning with latent label inference

In this paper, we unify a diverse set of learning tasks in NLP, semantic retrieval and related areas, under a common umbrella, which we call unsupervised semantic association learning (USAL). Examples of this generic task include word sense disambiguation, answer selection and question retrieval. We then present a novel modeling framework to tackle such tasks. The framework introduces, under the deep learning paradigm, a latent label indexing the true target in the candidate target set. An EM algorithm is then developed for learning the deep model and inferring the latent variables, principled under variational techniques and noise contrastive estimation. We apply the model and algorithm to several semantic retrieval benchmark tasks and the superior performance of the proposed approach is demonstrated via empirical studies.