Abstract
Question answering (QA) is a field of Natural Language Processing that deals with generating answers automatically to questions asked to a system. It can be categorized into two types—open-domain and closed-domain QA. Open-domain QA can deal with questions about anything, whereas closed-domain QA deals with questions in a specific domain. In our work, we use the architectures of LSTM and memory networks to perform closed-domain question answering and compare the performances of the two. LSTMs are specialized RNNs that can remember necessary data and forget the irrelevant bits. Since data in QA consist of stories and questions based on them, this model seems appropriate, with the ability to handle long sequences. On the other hand, memory networks provide an architecture where there is a provision to store the information learnt by the system in an explicit memory component, rather than just as weight matrices. This also seems like an architecture well-suited to question answering. We implement each model and train it on the Facebook bAbi dataset. This dataset is specifically generated for the purpose of evaluating QA systems on the twenty prerequisite toy bAbi tasks. Each dataset corresponds to one task and checks whether the model is able to perform chaining, counting, answer with single and multiple supporting facts, understand relations, directions, etc. Based on the performances of each model on the bAbi tasks, we perform a comparative study of the two.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kapashi, D., Shah, P.: Answering Reading Comprehension Using Memory Networks. Report for Stanford University Course cs224d (2015)
Das, B.: A Survey on Question Answering System, Survey for Indian Institute of Technology, Bombay (2014)
Hochreiter, S.: Untersuchungen zu dynamischen neuronalen Netzen [in German]. Diploma Thesis, TU Münich (1991)
Hochreiter, S., Schmidhuber, S.: Long short term memory. Neural Comput. 9(8), 1735–1780 (1997)
Understanding LSTM Networks: http://colah.github.io/posts/2015-08-Understanding-LSTMs/. Last accessed 4 Mar 2017
Hermann, K.M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., Blunsom, R.: Teaching machines to read and comprehend. In: 28th International Proceedings on Advances in Neural Information Processing Systems, pp. 1693–1701. MIT Press, Montreal (2015)
Weston, J., Chopra, S., Bordes, A.: Memory Networks, arXiv preprint, arXiv:1410.3916 (2014)
Weston, J., Bordes, A., Chopra, S., Rush, A.M., van Merriënboer, B., Joulin, A., Mikolov, T.: Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks, arXiv preprint, arXiv: 1502.05968 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rohit, G., Dharamshi, E.G., Subramanyam, N. (2019). Approaches to Question Answering Using LSTM and Memory Networks. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_15
Download citation
DOI: https://doi.org/10.1007/978-981-13-1592-3_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1591-6
Online ISBN: 978-981-13-1592-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)