The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Question Answering Chatbot Using Memory Networks
Abstract
Manipulation of a large amount of data becomes a very tedious task. Hence, the authors took the approach of memory networks for the implementation of the chatbot. Traditionally, the LSTM model was used to implement chatbots and QA systems. But the LSTM failed to store relevant information when given a longer information set. On the contrary, the memory networks have an additional memory component with it. This can help in storing long information for further use which is greatly advantageous for the QA and chatbot systems as compared to LSTM. The authors trained and tested their model over Facebook's bAbi dataset which consists of several tasks and has questions regarding each task to retrieve the accuracy of the model. On the pedestal of that dataset, they have presented the accuracy for every task in their study with memory networks.
Related Content
D. Lavanya, Divya Marupaka, Sandeep Rangineni, Shashank Agarwal, Latha Thammareddi, T. Shynu.
© 2024.
17 pages.
|
A. Sabarirajan, N. Arunfred, V. Bini Marin, Shouvik Sanyal, Rameshwaran Byloppilly, R. Regin.
© 2024.
14 pages.
|
P.S. Venkateswaran, M. Lishmah Dominic, Shashank Agarwal, Himani Oberai, Ila Anand, S. Suman Rajest.
© 2024.
16 pages.
|
Thangaraja Arumugam, R. Arun, R. Anitha, P. L. Swerna, R. Aruna, Vimala Kadiresan.
© 2024.
12 pages.
|
Thangaraja Arumugam, R. Arun, Sundarapandiyan Natarajan, Kiran Kumar Thoti, P. Shanthi, Uday Kiran Kommuri.
© 2024.
15 pages.
|
H. Hajra, G. Jayalakshmi.
© 2024.
17 pages.
|
H. Hajra, G. Jayalakshmi.
© 2024.
19 pages.
|
|
|