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Ensemble-based method of answers retrieval for domain specific questions from text-based documentation

机译：基于集成的基于文本的文档中针对特定领域问题的答案检索方法

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Many companies want or prefer to use chatbot systems to provide smart assistants for accompanying human specialists especially newbies with automatic consulting. Implementation of a really useful smart assistant for a specific domain requires a knowledge base for this domain, that often exists only in the form of text documentation and manuals.Lacks of properly built datasets and often expensiveness in resources and time to build one from scratch to apply data-driven methods with high quality. It motivates to seek a solution that can work without such data or require only a small amount of it though having reduced quality.The reformulation of the task into an information retrieval problem where the assistant responds with a piece of documentation instead of generated sentences may make the task easier but doesn’t solve the whole problem. It allows using of metrics-based methods with reduced search quality or data-driven methods which also needs a great amount of data.In this paper, we propose a new ensemble-based data-driven method that tries to learn a scoring function by combining independent functions from a predefined set. The method may substantially improve the quality of the search in comparison with pure metrics-based methods while requiring significantly less data for training than data-driven methods.
机译：许多公司希望或更喜欢使用聊天机器人系统为陪伴的人类专家（尤其是新手）提供自动咨询的智能助手。为特定领域实施真正有用的智能助手需要该领域的知识库，该知识库通常仅以文本文档和手册的形式存在。缺乏适当构建的数据集，以及从头到尾构建一个数据集的资源和时间通常很昂贵。高质量地应用数据驱动的方法。它的动机是寻求一种解决方案，该解决方案可以在没有此类数据的情况下工作，或者即使质量降低也只需要少量数据即可。将任务重新表述为信息检索问题，即助手用一张文档而不是生成的句子来回应这项任务比较容易，但不能解决整个问题。它允许使用降低搜索质量的基于指标的方法或也需要大量数据的数据驱动方法。预定义集合中的独立功能。与基于纯度量的方法相比，该方法可以显着提高搜索质量，同时与数据驱动的方法相比，所需的训练数据更少。

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