Large-scale retrieval is to recall relevant documents from a huge collection given a query. It relies on representation learning to embed documents and queries into a common semantic encoding space. According to the encoding space, recent retrieval methods based on pre-trained language models (PLM) can be coarsely categorized into either dense-vector or lexicon-based paradigms. These two paradigms unveil the PLMs' representation capability in different granularities, i.e., global sequence-level compression and local word-level contexts, respectively. Inspired by their complementary global-local contextualization and distinct representing views, we propose a new learning framework, UnifieR which unifies dense-vector and lexicon-based retrieval in one model with a dual-representing capability. Experiments on passage retrieval benchmarks verify its effectiveness in both paradigms. A uni-retrieval scheme is further presented with even better retrieval quality. We lastly evaluate the model on BEIR benchmark to verify its transferability.
@article{arxiv.2205.11194,
title = {UnifieR: A Unified Retriever for Large-Scale Retrieval},
author = {Tao Shen and Xiubo Geng and Chongyang Tao and Can Xu and Guodong Long and Kai Zhang and Daxin Jiang},
journal= {arXiv preprint arXiv:2205.11194},
year = {2023}
}