A More Robust Model to Answer Noisy Questions in KBQA

A More Robust Model to Answer Noisy Questions in KBQA

In practical applications, the raw input to a Knowledge Based Question Answering (KBQA) system may vary in forms, expressions, sources, etc. As a result, the actual input to the system may contain various errors caused by various noise in raw data and processes of transmission, transformation, trans...

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Bibliographic Details
Main Authors: Ziming Wang, Xirong Xu, Xinzi Li, Haochen Li, Li Zhu, Xiaopeng Wei
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Incremental learning
knowledge base question answering
machine learning
natural language processing
relation prediction
robustness
Online Access:https://ieeexplore.ieee.org/document/10058914/

Internet

https://ieeexplore.ieee.org/document/10058914/

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