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).

通过最小化词汇匹配问题，扩展增强的稀疏词汇表示改善了信息检索，并通过联合学习密集语义表示并将其与词汇表示结合来对候选信息进行排序的潜力进行了探讨。我们提出了一种混合信息检索机制，通过最大化词汇和语义匹配来最小化它们的缺点。我们的架构由独立编码查询和信息元素的双重混合编码器组成。每个编码器通过对应文本的可对比学习的术语扩展来联合学习密集语义表示和稀疏词汇表示。我们在包含在线产品页面上可用的典型异构信息的基准产品问答数据集上展示了我们模型的有效性。我们的评估表明，我们的混合方法在 MRR@5 分数上比独立训练的检索器提高了 10.95%（稀疏）和 2.7%（密集）。此外，我们的模型在减少响应时间（延迟）30% 和减少计算负载（FLOPs）约 38% 的同时，提供了更好的可解释性，并且表现与最先进的交叉编码器相当。