A Collaborative AI-enabled Pretrained Language Model for AIoT Domain Question Answering

Hongyin Zhu, Prayag Tiwari, Ahmed Ghoneim, M. Shamim Hossain

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)
156 Downloads (Pure)


Large-scale knowledge in the artificial intelligence of things (AIoT) field urgently needs effective models to understand human language and automatically answer questions. Pretrained language models achieve state-of-the-art performance on some question answering (QA) datasets, but few models can answer questions on AIoT domain knowledge. Currently, the AIoT domain lacks sufficient QA datasets and large-scale pretraining corpora. In this article, we propose RoBERTa_ AIoT to address the problem of the lack of high-quality large-scale labeled AIoT QA datasets. We construct an AIoT corpus to further pretrain RoBERTa and BERT. RoBERTa_ AIoT and BERT_ AIoT leverage unsupervised pretraining on a large corpus composed of AIoT-oriented Wikipedia webpages to learn more domain-specific context and improve performance on the AIoT QA tasks. To fine-tune and evaluate the model, we construct three AIoT QA datasets based on the community QA websites. We evaluate our approach on these datasets, and the experimental results demonstrate the significant improvements of our approach.

Original languageEnglish
Pages (from-to)3387-3396
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number5
Early online date14 Jul 2021
Publication statusPublished - May 2022
MoE publication typeA1 Journal article-refereed


  • AIoT
  • Question answering
  • RoBERTa
  • BERT
  • Domain-specific


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