Recent developments in representational learning for information retrieval
can be organized in a conceptual framework that establishes two pairs of
contrasts: sparse vs. dense representations and unsupervised vs. learned
representations. Sparse learned representations can further be decomposed into
expansion and term weighting components. This framework allows us to understand
the relationship between recently proposed techniques such as DPR, ANCE,
DeepCT, DeepImpact, and COIL, and furthermore, gaps revealed by our analysis
point to "low hanging fruit" in terms of techniques that have yet to be
explored. We present a novel technique dubbed "uniCOIL", a simple extension of
COIL that achieves to our knowledge the current state-of-the-art in sparse
retrieval on the popular MS MARCO passage ranking dataset. Our implementation
using the Anserini IR toolkit is built on the Lucene search library and thus
fully compatible with standard inverted indexes.

该论文使用一个概念框架将信息检索中的代表性学习的最新进展进行了组织，包括稀疏和密集表示、无监督和学习表示等四个维度。通过分析发现，COIL 和 DPR、ANCE、DeepCT、DeepImpact 之间的关系以及现有技术的不足之处，论文提出一种名为 uniCOIL 的新技术，并在 MS MARCO 排名数据集上取得了最先进的结果，具有可操作性。