@inproceedings{0b9f8fd7526c46e38e082269ef56238a,
title = "End-to-end memory networks: a survey",
abstract = "Constructing a dialog system which can speak naturally with a human is considered as a major challenge of artificial intelligence. End-to-end dialog system is taken to be a primary research topic in the area of conversational systems. Since an end-to-end dialog system is structured based on learning a dialog policy from transactional dialogs in a defined extent, therefore, useful datasets are required for evaluating the learning procedures. In this paper, different deep learning techniques are applied to the Dialog bAbI datasets. On this dataset, the performance of the proposed techniques is analyzed. The performance results demonstrate that all the proposed techniques attain decent precisions on the Dialog bAbI datasets. The best performance is obtained utilizing end-to-end memory network with a unified weight tying scheme (UN2N).",
keywords = "Deep learning, Dialog bAbI dataset, Memory networks",
author = "Raheleh Jafari and Sina Razvarz and Alexander Gegov",
year = "2020",
month = jul,
day = "4",
doi = "10.1007/978-3-030-52246-9\_20",
language = "English",
isbn = "9783030522452",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "291--300",
editor = "Kohei Arai and Supriya Kapoor and Rahul Bhatia",
booktitle = "Intelligent Computing - Proceedings of the 2020 Computing Conference",
address = "Netherlands",
note = "Science and Information Conference, SAI 2020 ; Conference date: 16-07-2020 Through 17-07-2020",
}