Conditional variational autoencoder for query expansion in ad-hoc information retrieval

Abstract

Query expansion (QE) is commonly used to improve the performance of traditional information retrieval (IR) models. With the adoption of deep learning in IR research, neural QE models have emerged in recent years. Many of these models focus on learning embeddings by leveraging query-document relevance. These embedding models allow computing semantic similarities between queries and documents to generate expansion terms. However, existing models often ignore query-document interactions. This research aims to address that gap by proposing a QE model using a conditional variational autoencoder. It first maps a query-document pair into a latent space based on their interaction, then estimates an expansion model from that latent space. The proposed model is trained on relevance feedback data and generates expansions using pseudo-relevance feedback at test time. The proposed model is evaluated on three standard TREC collections for document ranking: AP and Robust 04 and GOV02, and the MS MARCO dataset for passage ranking. Results show the model outperforms state-of-the-art traditional and neural QE models. It also demonstrates higher additivity with neural matching than baselines.