Abstract:
Continuous customer relationship plays an important role in the success of any business milieus in today’s world. Nonetheless, it can be harder to achieve consistent engagement with the customers round the clock and therefore many businesses have paved their focus in using a variety of solutions in overcoming this scenario. Contextual assistants that can have both linear and non-linear conversations with humans implicitly plays a prominent role in such situations. In contrast to resource-rich languages, creating a contextual assistant for resource-poor languages like Sinhala has been difficult mainly due to the unavailability of a rich digital footprint and the complexity of the language. Hence, this research was conducted to propose and implement a novel and common architecture of a contextual assistant framework for the Sinhala language. Here we have used a deep learning Intent Mapping (IM) model to map the consumer response to a predefined “Intent” and a Feature Extraction Mechanism (FEM) to extract related information from the input text. A set of data types for this framework were defined and FEM was trained to identify them efficiently. The IM model gave an accuracy output of 89.67 percent. The results depicted that the implemented system performs with higher accuracy in linear conversations.
