This paper presents first successful steps in designing search agents that
learn meta-strategies for iterative query refinement in information-seeking
tasks. Our approach uses machine reading to guide the selection of refinement
terms from aggregated search results. Agents are then empowered with simple but
effective search operators to exert fine-grained and transparent control over
queries and search results. We develop a novel way of generating synthetic
search sessions, which leverages the power of transformer-based language models
through (self-)supervised learning. We also present a reinforcement learning
agent with dynamically constrained actions that learns interactive search
strategies from scratch. Our search agents obtain retrieval and answer quality
performance comparable to recent neural methods, using only a traditional
term-based BM25 ranking function and interpretable discrete reranking and
filtering actions.

本文介绍了设计搜索代理的成功步骤，通过机器学习元策略来迭代查询精细化。文章提出了一种新颖的方法，使用机器阅读辅助挑选查询结果的精炼术语，使代理能够通过简单而有效的搜索操作对查询和搜索结果进行细粒度和透明的控制。通过自主学习，利用基于 Transformer 的语言模型来生成综合搜索会话，并介绍了一种强化学习代理，其具有动态限制的动作，可以从零开始学习交互式搜索策略。我们的搜索代理只使用传统的基于词项的 BM25 排名函数和可解释的离散筛选和筛选操作，即可达到与最新的神经方法相当的检索和答案质量性能。