Non-Factoid Answer Selection in Indonesian Science Question Answering System using Long Short-Term Memory (LSTM)

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Abstract

Understanding natural science has become vital since it has effects on various facets of daily necessity. However, the lack of comprehensive information sources has led to difficulties in finding information quickly and efficiently, emphasising the need to develop a question answering system. The type of question utilised for question answering systems that specifically deal with science topics is referred to as a non-factoid question. In this research, the question answering system utilised Long Short-Term Memory (LSTM) model for answer selection problems. Testing was done for the following LSTM hyper-parameters: dropouts, learning rates, number of hidden units, size of the answer pool, and margins. Data utilised in this study consisted of 400 pairs of questions and answers on science topics that were obtained from Wikipedia. The highest average values were 90.06% for Mean Reciprocal Rank (MRR) and 78.69% for Mean Average Precision (MAP), which were achieved when using a dropout value of 0.2, 50 hidden units, learning rate value of 0.05, a margin of 0.1, and an answer pool size of 20.

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