Forming of Dyadic Conversation Dataset for Bahasa Indonesia
Cuk Tho, Arden Sagiterry Setiawan, Andry Chowanda · Procedia Computer Science · 2018
Computer has been assisting human in almost all aspects of daily life. Despite the computer outsmart human in some tasks, it is a social ignorant tool. It does not understand and capable of doing natural conversation with human. To build a system that can naturally understand and communicate with human, it is essential to train the system with natural conversation data. This paper proposes a dataset which consists of natural dyadic conversation in Indonesian language. Where literature suggested that there are exists inadequate number of conversation dataset in Indonesian Language. There are 3164 words (formal and slang (informal nonstandard words) annotated from the recording of five groups, with the largest number of words belong to Food topic (Group 3, 826 words), and the lowest one belongs to Travelling topic (Group 2, 372 words). The dataset contributes a pre-trained conversation model with deep learning (LSTM). The model is trained in 10000 iterations, 128 batches, and 4 hidden layers, resulted in a perplexity of 2.01.