TY - GEN
T1 - Robustness Analysis on Natural Language Processing Based AI Q&A Robots
AU - Yuan, Chengxiang
AU - Xue, Mingfu
AU - Zhang, Lingling
AU - Wu, Heyi
N1 - Publisher Copyright:
© 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2019
Y1 - 2019
N2 - Recently, the natural language processing (NLP) based intelligent question and answering (Q&A) robots have been used in a wide range of applications, such as smart assistant, smart customer service, government business. However, the robustness and security issues of these NLP based artificial intelligence (AI) Q&A robots have not been studied yet. In this paper, we analyze the robustness problems in current Q&A robots, which include four aspects: (1) semantic slot settings are incomplete; (2) sensitive words are not filtered efficiently and completely; (3) Q&A robots return the search results directly; (4) unsatisfactory matching algorithms and inappropriate matching threshold settings. Then, we design and implement two types of evaluation tests, bad language and user’s typos, to evaluate the robustness of several state-of-the-art Q&A robots. Experiment results show that these common inputs (bad language and user’s typos) can successfully make these Q&A robots malfunction, denial of service, or speaking dirty words. Besides, we also propose possible countermeasures to enhance the robustness of these Q&A robots. To the best of the authors’ knowledge, this is the first work on analyzing the robustness and security problems of intelligent Q&A robots. This work can hopefully help provide guidelines to design robust and secure Q&A robots.
AB - Recently, the natural language processing (NLP) based intelligent question and answering (Q&A) robots have been used in a wide range of applications, such as smart assistant, smart customer service, government business. However, the robustness and security issues of these NLP based artificial intelligence (AI) Q&A robots have not been studied yet. In this paper, we analyze the robustness problems in current Q&A robots, which include four aspects: (1) semantic slot settings are incomplete; (2) sensitive words are not filtered efficiently and completely; (3) Q&A robots return the search results directly; (4) unsatisfactory matching algorithms and inappropriate matching threshold settings. Then, we design and implement two types of evaluation tests, bad language and user’s typos, to evaluate the robustness of several state-of-the-art Q&A robots. Experiment results show that these common inputs (bad language and user’s typos) can successfully make these Q&A robots malfunction, denial of service, or speaking dirty words. Besides, we also propose possible countermeasures to enhance the robustness of these Q&A robots. To the best of the authors’ knowledge, this is the first work on analyzing the robustness and security problems of intelligent Q&A robots. This work can hopefully help provide guidelines to design robust and secure Q&A robots.
KW - AI security
KW - Natural language processing
KW - Question and answer robots
KW - Robustness
UR - https://www.scopus.com/pages/publications/85076159990
U2 - 10.1007/978-3-030-32388-2_57
DO - 10.1007/978-3-030-32388-2_57
M3 - 会议稿件
AN - SCOPUS:85076159990
SN - 9783030323875
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 695
EP - 711
BT - Machine Learning and Intelligent Communications - 4th International Conference, MLICOM 2019, Proceedings
A2 - Zhai, Xiangping Bryce
A2 - Chen, Bing
A2 - Zhu, Kun
PB - Springer
T2 - 4th International Conference on Machine Learning and Intelligent Communications, MLICOM 2019
Y2 - 24 August 2019 through 25 August 2019
ER -