This paper describes a compact and effective model for low-latency passage
retrieval in conversational search based on learned dense representations.
Prior to our work, the state-of-the-art approach uses a multi-stage pipeline
comprising conversational query reformulation and information retrieval
modules. Despite its effectiveness, such a pipeline often includes multiple
neural models that require long inference times. In addition, independently
optimizing each module ignores dependencies among them. To address these
shortcomings, we propose to integrate conversational query reformulation
directly into a dense retrieval model. To aid in this goal, we create a dataset
with pseudo-relevance labels for conversational search to overcome the lack of
training data and to explore different training strategies. We demonstrate that
our model effectively rewrites conversational queries as dense representations
in conversational search and open-domain question answering datasets. Finally,
after observing that our model learns to adjust the $L_2$ norm of query token
embeddings, we leverage this property for hybrid retrieval and to support error
analysis.

本文以学习到的密集表示为基础，描述了一种紧凑而有效的模型，用于对话搜索中的低延迟段落检索。 通过将对话查询重构直接整合到密集检索模型中，我们提出了一种新方法来解决现有流程的问题。 在构建具有伪相关性标签的数据集的基础上，证明了该模型可用于会话检索和开放域问答数据集，最后通过改进的检索模型学习到的特性，支持混合检索和错误分析。