Intelligent conversation system using multiple classification ripple down rules and conversational context

We introduce an extension to Multiple Classification Ripple Down Rules (MCRDR), called Contextual MCRDR (C-MCRDR). We apply C-MCRDR knowledge-base systems (KBS) to the Textual Question Answering (TQA) and Natural Language Interface to Databases (NLIDB) paradigms in restricted domains as a type of spoken dialog system (SDS) or conversational agent (CA). C-MCRDR implicitly maintains topical conversational context, and intra-dialog context is retained allowing explicit referencing in KB rule conditions and classifications. To facilitate NLIDB, post-inference C-MCRDR classifications can include generic query referencing - query specificity is achieved by the binding of pre-identified context. In contrast to other scripted, or syntactically complex systems, the KB of the live system can easily be maintained courtesy of the RDR knowledge engineering approach. For evaluation, we applied this system to a pedagogical domain that uses a production database for the generation of offline course-related documents. Our system complemented the domain by providing a spoken or textual question-answering alternative for undergraduates based on the same production database. The developed system incorporates a speech-enabled chatbot interface via Automatic Speech Recognition (ASR) and experimental results from a live, integrated feedback rating system showed significant user acceptance, indicating the approach is promising, feasible and further work is warranted. Evaluation of the prototype's viability found the system responded appropriately for 80.3% of participant requests in the tested domain, and it responded inappropriately for 19.7% of requests due to incorrect dialog classifications (4.4%) or out of scope requests (15.3%). Although the semantic range of the evaluated domain was relatively shallow, we conjecture that the developed system is readily adoptable as a CA NLIDB tool in other more semantically-rich domains and it shows promise in single or multi-domain environments. (C) 2018 The Authors. Published by Elsevier Ltd.

我们引入了对多分类涟漪下降规则（MCRDR）的一种扩展，称为情境多分类涟漪下降规则（C - MCRDR）。我们将C - MCRDR知识库系统（KBS）应用于受限领域的文本问答（TQA）和自然语言数据库接口（NLIDB）范式，作为一种口语对话系统（SDS）或会话智能体（CA）。C - MCRDR隐式地维护主题对话情境，并且保留对话内情境，允许在知识库规则条件和分类中进行显式引用。为了便于NLIDB，推理后的C - MCRDR分类可以包括通用查询引用——通过绑定预先确定的情境来实现查询的特异性。与其他脚本化或语法复杂的系统相比，由于涟漪下降规则（RDR）知识工程方法，实时系统的知识库可以很容易地维护。为了进行评估，我们将该系统应用于一个教学领域，该领域使用一个生产数据库来生成离线的课程相关文档。我们的系统基于相同的生产数据库为本科生提供了口语或文本问答的替代方式，从而对该领域进行了补充。所开发的系统通过自动语音识别（ASR）集成了一个支持语音的聊天机器人界面，并且来自一个实时集成反馈评级系统的实验结果显示用户有很高的接受度，这表明该方法是有前景的、可行的，值得进一步研究。对原型可行性的评估发现，在测试领域中，系统对80.3%的参与者请求做出了适当响应，而对于19.7%的请求响应不当，这是由于对话分类错误（4.4%）或超出范围的请求（15.3%）。尽管所评估领域的语义范围相对较浅，但我们推测所开发的系统很容易作为一种CA NLIDB工具被应用于其他语义更丰富的领域，并且在单领域或多领域环境中都有应用前景。（C）2018作者。由爱思唯尔有限公司出版。