Question Answering Using Deep Learning

5 Pages Posted: 9 Sep 2019

See all articles by Preena M P

Preena M P

Government Engineering College, Palakkad - Department of Computer Science and Engineering

Shibily

Government Engineering College, Sreekrishnapuram

Date Written: September 4, 2019

Abstract

Question answering (QA) is a well-researched problem in NLP. QA application are information retrieval and entity extraction. The paper propose long short-term memory (LSTM) model for text-based question answering where questions are based on a particular sentence. Given a sentences and a question to the model for predict the correct answer. Here question answering using memory network (MN). The memory network including 4 components are input, generalization, output, response. Memory network is the long term memory is required to read a story and then answer the questions. Input is sentences, options and the questions. First passes the question and sentences to the memory model. Using score function assigns a score to each question-sentences pair and get more similar sentences. After the result is matched to the supporting factors using score function. To get the correct answer. Here used the facebook babi dataset.

Keywords: Memory Network(MN), Long Short-Term Memory (LSTM)

Suggested Citation

M P, Preena and Joseph, Shibily, Question Answering Using Deep Learning (September 4, 2019). In proceedings of the International Conference on Systems, Energy & Environment (ICSEE) 2019, GCE Kannur, Kerala, July 2019. Available at SSRN: https://ssrn.com/abstract=3447734 or http://dx.doi.org/10.2139/ssrn.3447734

Preena M P (Contact Author)

Government Engineering College, Palakkad - Department of Computer Science and Engineering ( email )

India

Shibily Joseph

Government Engineering College, Sreekrishnapuram ( email )

India

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