Abstract:
"With recent developments in the domain of Natural Language Processing (NLP) most of the
day-to-day activities that involve natural language processing have been automated, including
tasks like writing scripts, writing emails, customer support services, language translation etc.
But in languages like Sinhala, those novel threads and techniques have yet to be applied and
adapted because of language ambiguity and the lack of resources. Developing Sinhala language
processing can contribute to its future advancements in areas such as text generation, chatbot
applications, text summarization, language translations and etc.
This research focused on improving the Sinhala NLP domain by introducing a novel machine
learning architecture called the Transformers architecture to chatbot application systems. This
will increase the accuracy of Sinhala language chatbot systems. The Novel architecture
introducing from this research study is a hybrid approach, which SinBERT and a LSTM layer
combine to improve the accuracy of the model"
