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首页> 外文期刊>Kunstliche Intelligenz >Connecting Question Answering and Conversational Agents - Contextualizing German Questions for Interactive Question Answering Systems
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Connecting Question Answering and Conversational Agents - Contextualizing German Questions for Interactive Question Answering Systems

机译：连接问答和对话代理-交互式问答系统的德语问题的语境化

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Research results in the field of Question Answering (QA) have shown that the classification of natural language questions significantly contributes to the accuracy of the generated answers. In this paper we present an approach which extends the prevalent question classification techniques by additionally considering further contextual information provided by the questions. Thereby we focus on improving the conversational abilities of existing interactive interfaces by enhancing their underlying QA systems in terms of response time and correctness. As a result, we are able to introduce a method based on a tripartite contextualization. First, we present a comprehensive question classification experiment based on machine learning using two different datasets and various feature sets for the German language. Second, we propose a method for detecting the focus chunk of a given question, that is, for identifying which part of the question is fundamentally relevant to the answer and which part refers to a specification of it. Third, we investigate how to identify and label the topic of a given question by means of a human-judgment experiment. We show that the resulting contextualization method contributes to an improvement of existing question answering systems and enhances their application within interactive scenarios.
机译：问题解答（QA）领域的研究结果表明，自然语言问题的分类对提高所生成答案的准确性有很大贡献。在本文中，我们提出了一种方法，通过额外考虑问题提供的其他上下文信息来扩展流行的问题分类技术。因此，我们专注于通过增强响应时间和正确性方面的基础QA系统来提高现有交互界面的会话能力。结果，我们能够介绍一种基于三重上下文的方法。首先，我们提出了一个基于机器学习的综合性问题分类实验，使用了两个不同的数据集和德语的各种功能集。其次，我们提出了一种方法，用于检测给定问题的焦点部分，即识别该问题的哪一部分与答案基本相关，以及哪一部分指的是其说明。第三，我们研究如何通过人类判断实验来识别和标记给定问题的主题。我们表明，结果化的语境化方法有助于改善现有的问答系统并增强其在交互式场景中的应用。

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