Although exact term match between queries and documents is the dominant
method to perform first-stage retrieval, we propose a different approach,
called RepBERT, to represent documents and queries with fixed-length
contextualized embeddings. The inner products of query and document embeddings
are regarded as relevance scores. On MS MARCO Passage Ranking task, RepBERT
achieves state-of-the-art results among all initial retrieval techniques. And
its efficiency is comparable to bag-of-words methods.

使用 RepBERT 来进行文档和查询的表示，通过上下文嵌入的固定长度实现，使用嵌入的内积作为相关性分数，在 MS MARCO Passage Ranking 任务中，RepBERT 在所有初始检索技术中实现了最新的结果。

RepBERT: 一阶段检索的上下文化文本嵌入

RepBERT: Contextualized Text Embeddings for First-Stage Retrieval

We propose several small modifications to Duet---a deep neural ranking
model---and evaluate the updated model on the MS MARCO passage ranking task. We
report significant improvements from the proposed changes based on an ablation
study.

本文提出了对 Duet 深度神经排名模型的若干小修改，并在 MS MARCO 文章排名任务上进行了评估，根据消融研究，我们报告了所提出的改进的显著提升。