Knowledge-based chatbot (KBC) has grown in popularity in recent years and has been widely used for various use cases. Building KBC from scratch using deep learning (DL) is challenging since no prior historical data exists. Meanwhile, DL systems need a vast Volume of data to be trained. This paper proposes a novel framework to create an intent classifier of the KBC used to detect in-scope (IS) and out-of-scope (OOS) intents. We introduce an automated queries generator to create IS intents employed as the training data from an ontology input. We utilize Bidirectional Encode Representations from Transformers (BERT) fine-tuning as the backbone of our DL system. Moreover, we present a Bayesian approach as an extension of the BERT to classify OOS queries with minimal OOS training data. The experiments result show that the proposed method manages to achieve an F1 score of 100% for IS intents and 86% for OOS queries.
A proper use of design patterns has proven to be very useful in the development of robust applications over time. In this paper, the design patterns are introduced in the early stage of the software development where model-driven architecture is used as the engineering approach. A RESTful internet payment gateway API (Application Programming Interface) wrapper is selected as the case study. At the beginning, Platform Independent Model (PIM) is created as the domain model. After that, the PIM is transformed into the Platform Specific Model (PSM). Before converting the PSM into the source code, three design patterns such as builder, observer, and factory pattern are added into the model. To evaluate the impacts of implementation, static analysis is used to examine the generated code before and after adding the design patterns. The result shows that the design decision increases cohesion, complexity, coupling, inheritance, and size metrics of the source codes.
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