Learning to search is the task of building artificial agents that learn to
autonomously use a search box to find information. So far, it has been shown
that current language models can learn symbolic query reformulation policies,
in combination with traditional term-based retrieval, but fall short of
outperforming neural retrievers. We extend the previous learning to search
setup to a hybrid environment, which accepts discrete query refinement
operations, after a first-pass retrieval step performed by a dual encoder.
Experiments on the BEIR task show that search agents, trained via behavioral
cloning, outperform the underlying search system based on a combined dual
encoder retriever and cross encoder reranker. Furthermore, we find that simple
heuristic Hybrid Retrieval Environments (HRE) can improve baseline performance
by several nDCG points. The search agent based on HRE (HARE) produces
state-of-the-art performance on both zero-shot and in-domain evaluations. We
carry out an extensive qualitative analysis to shed light on the agents
policies.

本文主要研究的是学习搜索领域中，通过基于符号的查询重构策略组合基于词项的传统检索来学习人工智能搜索引擎检索的能力，同时也拓展到了混合检索环境来进行离散查询操作，本文的实验证明了在这种混合环境下，基于 HRE 的搜索代理在 BEIR 任务中优于传统方法。

基于搜索智能体和混合环境的零次检索

Zero-Shot Retrieval with Search Agents and Hybrid Environments

Document-level Event Extraction (DEE) is particularly tricky due to the two
challenges it poses: scattering-arguments and multi-events. The first challenge
means that arguments of one event record could reside in different sentences in
the document, while the second one reflects one document may simultaneously
contain multiple such event records. Motivated by humans' reading cognitive to
extract information of interests, in this paper, we propose a method called HRE
(Human Reading inspired Extractor for Document Events), where DEE is decomposed
into these two iterative stages, rough reading and elaborate reading.
Specifically, the first stage browses the document to detect the occurrence of
events, and the second stage serves to extract specific event arguments. For
each concrete event role, elaborate reading hierarchically works from sentences
to characters to locate arguments across sentences, thus the
scattering-arguments problem is tackled. Meanwhile, rough reading is explored
in a multi-round manner to discover undetected events, thus the multi-events
problem is handled. Experiment results show the superiority of HRE over prior
competitive methods.

本文提出了基于人类阅读认知启发的文档事件抽取方法 HRE（Human Reading inspired Extractor for Document Events），通过将 DEE 分解为两个迭代阶段 ——rough reading 和 elaborate reading，逐步实现对于文本中散布的事件和多个事件的精细抽取。实验结果表明 HRE 优于之前竞争方法。