A Context Alignment Mechanism and Recurrent Neural Networks For Information Retrieval

To overcome the failure of bag-of-words approach in Information Retrieval (IR), recently IR researchers lead toward the use of deep learning models to learn the query and document representations. The relevance score of a document given a query is defined as the distance between the two representations, it can be binary: relevant or not relevant, or on a scale from very relevant to not at all relevant. In this paper, we use the Context-Aligned Recurrent Neural Networks model (CA-RNN) proposed by Qin et al. (2018) to learn low dimension representations of queries and documents. The CA-RNN model is based on a context alignment mechanism and Recurrent Neural Networks (RNN) model. Given a query, documents are ranked using the relevance score. The evaluation results on two small datasets extracted from TREC Desk4 and Desk 5 show a net outperformance of CA-RNN over traditional models like BM25, LSA, BERT and BiLSTM. The context alignment mechanism used in CA-RNN can improve significantly the representations learning to rank IR systems.