SQE-GAN: A Supervised Query Expansion Scheme via GAN

Existing Supervised Query Expansion (SQE) spends much time in term feature extraction but generates sub-optimal expanded terms. In this paper, we introduce Generative Adversarial Nets (GANs) and propose a GAN-based SQE method (SQE-GAN) to get helpful query expansion terms. We unify two types of models in query expansion: the generative model and the discriminative one. The generative (resp., discriminative) model focuses on predicting relevant terms (resp., relevancy) given a query (resp., a query-term pair). We iteratively optimize both models with a game between them. Besides, a BiLSTM layer is adopted to encode the utility of a term with respect to the query. As a result, the costly feature calculation in SQE schemes is avoided, such that the efficiency can be significantly improved. Moreover, by introducing GAN into expansion, the expanded terms are possible to be more effective with respect to the eventual needs of the user. Our experimental results demonstrate that SQE-GAN can be 37.3% faster than state-of-the-art SQE solutions while outperforming some recently proposed neural models in the retrieval quality.