3168-2024 - IEEE Standard for Robustness Evaluation Test Methods for a Natural Language Processing Service That Uses Machine Learning / Institute of Electrical and Electronics Engineers.

Format:

Book

Author/Creator:

Institute of Electrical and Electronics Engineers, author, issuing body.

Language:

English

Subjects (All):

Machine learning--Congresses.

Machine learning.

Physical Description:

1 online resource

Place of Publication:

[Place of publication not identified] : Institute of Electrical and Electronics Engineers, 2024.

Summary:

The natural language processing (NLP) services using machine learning have rich applications in solving various tasks and have been widely deployed and used, usually accessible by application programming interface (API) calls. The robustness of the NLP services is challenged by various well-known general corruptions and adversarial attacks. Inadvertent or random deletion, addition, or repetition of characters or words are examples of general corruptions. Adversarial characters, words, or sentence samples are generated by adversarial attacks, causing the models underpinning the NLP services to produce incorrect results. A method for quantitatively evaluating.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9798855709100