Ranking has always been one of the top concerns in information retrieval
research. For decades, lexical matching signal has dominated the ad-hoc
retrieval process, but it also has inherent defects, such as the vocabulary
mismatch problem. Recently, Dense Retrieval (DR) technique has been proposed to
alleviate these limitations by capturing the deep semantic relationship between
queries and documents. The training of most existing Dense Retrieval models
relies on sampling negative instances from the corpus to optimize a pairwise
loss function. Through investigation, we find that this kind of training
strategy is biased and fails to optimize full retrieval performance effectively
and efficiently. To solve this problem, we propose a Learning To Retrieve
(LTRe) training technique. LTRe constructs the document index beforehand. At
each training iteration, it performs full retrieval without negative sampling
and then updates the query representation model parameters. Through this
process, it teaches the DR model how to retrieve relevant documents from the
entire corpus instead of how to rerank a potentially biased sample of
documents. Experiments in both passage retrieval and document retrieval tasks
show that: 1) in terms of effectiveness, LTRe significantly outperforms all
competitive sparse and dense baselines. It even gains better performance than
the BM25-BERT cascade system under reasonable latency constraints. 2) in terms
of training efficiency, compared with the previous state-of-the-art DR method,
LTRe provides more than 170x speed-up in the training process. Training with a
compressed index further saves computing resources with minor performance loss.

信息检索一直是信息检索研究的重点之一。近年来，提出了 Dense Retrieval（DR）技术来缓解诸如词汇不匹配问题等固有缺陷，但是绝大多数现有 DR 模型的培训都依赖于从语料库中抽样负实例来优化成对损失函数，即不公平的样本，因此提出了 Learning To Retrieve（LTRe）培训技术， 它通过预先构建文档索引，并在每个培训迭代中在没有负样本采样的情况下执行全检索，从而在整个语料库中检索相关文档，实验表明，LTRe 在有效性方面明显优于所有有竞争力的稀疏和密集基线。它甚至在合理的延迟限制下比 BM25-BERT 级联系统表现更好。