Many people use search engines to find online guidance to solve computer or
mobile device problems. Users frequently encounter challenges in identifying
effective solutions from search results, often wasting time trying ineffective
solutions that seem relevant yet fail to solve the real problems. This paper
introduces a novel approach to improving the accuracy and relevance of online
technical support search results through automated search results verification
and reranking. Taking "How-to" queries specific to on-device execution as a
starting point, we first developed a solution that allows an AI agent to
interpret and execute step-by-step instructions in the search results in a
controlled Android environment. We further integrated the agent's findings into
a reranking mechanism that orders search results based on the success
indicators of the tested solutions.
The paper details the architecture of our solution and a comprehensive
evaluation of the system through a series of tests across various application
domains. The results demonstrate a significant improvement in the quality and
reliability of the top-ranked results. Our findings suggest a paradigm shift in
how search engine ranking for online technical support help can be optimized,
offering a scalable and automated solution to the pervasive challenge of
finding effective and reliable online help.

通过自动化搜索结果验证和重新排序的方法，该论文提出了改善在线技术支持搜索结果准确性和相关性的新方法。通过解释和执行搜索结果中的逐步说明，并将搜索结果成功指标整合到重新排序机制中，论文详细介绍了解决方案的架构和全面评估结果。实验结果表明，在排名靠前的结果上质量和可靠性有显著改善，为解决在找到有效和可靠在线帮助方面的普遍挑战提供了可扩展和自动化的解决方案。

通过自动化搜索结果验证和重新排序提高技术 “如何做” 查询的准确性

Improving Technical "How-to" Query Accuracy with Automated Search  Results Verification and Reranking

Technical support problems are often long and complex. They typically contain
user descriptions of the problem, the setup, and steps for attempted
resolution. Often they also contain various non-natural language text elements
like outputs of commands, snippets of code, error messages or stack traces.
These elements contain potentially crucial information for problem resolution.
However, they cannot be correctly parsed by tools designed for natural
language. In this paper, we address the problem of segmentation for technical
support questions. We formulate the problem as a sequence labelling task, and
study the performance of state of the art approaches. We compare this against
an intuitive contextual sentence-level classification baseline, and a state of
the art supervised text-segmentation approach. We also introduce a novel
component of combining contextual embeddings from multiple language models
pre-trained on different data sources, which achieves a marked improvement over
using embeddings from a single pre-trained language model. Finally, we also
demonstrate the usefulness of such segmentation with improvements on the
downstream task of answer retrieval.

本文介绍了一种利用多种语言模型来实现针对技术支持问题的分割，以提高解决问题的效率，并通过案例分析证明了这种分割可以提高问题回答的准确性。