Jointly Learning Span Extraction and Sequence Labeling for Information Extraction from Business Documents

Sơn, Nguyễn Hồng; Yu, Hieu M.; Nguyen, Tuan-Anh; Nguyen, Minh-Tien

doi:10.1109/ijcnn55064.2022.9892779

Cited by 5 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FastQA Apart from BERT-QA, we also tested FastQA (Son et al, 2022). While BERT-QA extracts each cause or effect span independently, FastQA extracts cause and effect simultaneously as a pair.…”

Section: Cause-effect Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures

Inoue,

Nguyen,

Mizokuchi

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

View full text Add to dashboard Cite

This paper introduces a new IncidentAI dataset for safety prevention. Different from prior corpora that usually contain a single task, our dataset comprises three tasks: named entity recognition, cause-effect extraction, and information retrieval. The dataset is annotated by domain experts who have at least six years of practical experience as high-pressure gas conservation managers. We validate the contribution of the dataset in the scenario of safety prevention. Preliminary results on the three tasks show that NLP techniques are beneficial for analyzing incident reports to prevent future failures. The dataset facilitates future research in NLP and incident management communities. The access to the dataset is also provided. 1

show abstract

Section: Cause-effect Extractionmentioning

confidence: 99%

“…The model was trained in 5 epochs, with the learning rate of 5e − 5, and the batch size of 16. FastQA (Son et al, 2022) and Guided-QA were trained using the source code from each paper. Again, FastQA and Guided-QA were trained in 5 epochs with the learning rate of 5e − 5 and the batch size of 16.…”

Section: A42 Cause-effect Extraction Modelsmentioning

confidence: 99%