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

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.

[1]  Farid Sufian Shuaib.  Comparative Law in Asia: The Case for Intra-Asia Intensification , 2018, ICLR 2018.

[2]  Ayu Purwarianti,et al.  Employing Dependency Tree in Machine Learning Based Indonesian Factoid Question Answering , 2019, Jurnal Linguistik Komputasional (JLK).

[3]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[4]  Christopher D. Manning,et al.  Probabilistic Tree-Edit Models with Structured Latent Variables for Textual Entailment and Question Answering , 2010, COLING.

[5]  Retno Kusumaningrum,et al.  Word2Vec for Indonesian Sentiment Analysis towards Hotel Reviews: An Evaluation Study , 2019, Procedia Computer Science.

[6]  Jason Weston,et al.  A Neural Attention Model for Abstractive Sentence Summarization , 2015, EMNLP.

[7]  Bowen Zhou,et al.  Applying deep learning to answer selection: A study and an open task , 2015, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU).

[8]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[9]  Ming-Wei Chang,et al.  Question Answering Using Enhanced Lexical Semantic Models , 2013, ACL.

[10]  Ayu Purwarianti,et al.  SISTEM QUESTION ANSWERING BAHASA INDONESIA UNTUK PERTANYAAN NON-FACTOID , 2012 .

[11]  Ting Liu,et al.  Document Modeling with Gated Recurrent Neural Network for Sentiment Classification , 2015, EMNLP.

[12]  Sheng Li,et al.  A Review on Deep Learning Techniques Applied to Answer Selection , 2018, COLING.

[13]  Jason Weston,et al.  A Neural Attention Model for Sentence Summarization , 2015 .

[14]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[15]  W. Bruce Croft,et al.  aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model , 2016, CIKM.

[16]  Bowen Zhou,et al.  LSTM-based Deep Learning Models for non-factoid answer selection , 2015, ArXiv.