Can I read Tele-Knowledge Pre-training for Fault Analysis on EtoBox?

Tele-Knowledge Pre-training for Fault Analysis by Chen, Zhuo; Zhang, Wen; Huang, Yufeng; Chen, Mingyang; Geng, Yuxia; Yu, Hongtao; Bi, Zhen; Zhang, Yichi; Yao, Zhen; Song, Wenting; Wu, Xinliang; Yang, Yi; Chen, Mingyi; Lian, Zhaoyang; Li, Yingying; Cheng, Lei; Chen, Huajun is a scholarly article available to read on EtoBox.

What is Tele-Knowledge Pre-training for Fault Analysis about?

In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents. To organize this knowledge from experts uniformly, we propose to create a Tele-KG (tele-knowledge graph). Using this valuable data, we further propose a tele-domain language pre-training model TeleBERT and its knowledge-enhanced version, a tele-knowledge re-training model KTeleBERT. which includes effective prompt hints, adaptive numerical data encoding, and two knowledge injection paradigms. Concretely, our proposal includes two stages: first, pre-training TeleBERT on 20 million tele-related corpora, and then re-training it on 1 million causal and machine-related corpora to obtain KTeleBERT. Our evaluation on multiple tasks related to fault analysis in tele-applications, including root-cause analysis, event association prediction, and fault chain tracing, shows that pre-training a language model with tele-domain data is beneficial for downstream tasks. Moreover, the KTeleBERT re-training further improves the performance of task models, highligh

Author

Chen, Zhuo; Zhang, Wen; Huang, Yufeng; Chen, Mingyang; Geng, Yuxia; Yu, Hongtao; Bi, Zhen; Zhang, Yichi; Yao, Zhen; Song, Wenting; Wu, Xinliang; Yang, Yi; Chen, Mingyi; Lian, Zhaoyang; Li, Yingying; Cheng, Lei; Chen, Huajun

Published

2022

Language

EN