  Expansion-enhanced sparse lexical representation improves information
retrieval (IR) by minimizing vocabulary mismatch problems during lexical
matching. In this paper, we explore the potential of jointly learning dense
semantic representation and combining it with the lexical one for ranking
candidate information. We present a hybrid information retrieval mechanism that
maximizes lexical and semantic matching while minimizing their shortcomings.
Our architecture consists of dual hybrid encoders that independently encode
queries and information elements. Each encoder jointly learns a dense semantic
representation and a sparse lexical representation augmented by a learnable
term expansion of the corresponding text through contrastive learning. We
demonstrate the efficacy of our model in single-stage ranking of a benchmark
product question-answering dataset containing the typical heterogeneous
information available on online product pages. Our evaluation demonstrates that
our hybrid approach outperforms independently trained retrievers by 10.95%
(sparse) and 2.7% (dense) in MRR@5 score. Moreover, our model offers better
interpretability and performs comparably to state-of-the-art cross encoders
while reducing response time by 30% (latency) and cutting computational load by
approximately 38% (FLOPs).
