کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6854767 | 1437594 | 2018 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Intelligent conversation system using multiple classification ripple down rules and conversational context
ترجمه فارسی عنوان
سیستم مکالمه هوشمند با استفاده از طبقه بندی چندگانه موجب کاهش قوانین و زمینه مکالمه می شود
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 112, 1 December 2018, Pages 342-352
Journal: Expert Systems with Applications - Volume 112, 1 December 2018, Pages 342-352
نویسندگان
David Herbert, Byeong Ho Kang,