activity
20212023
most citedEnd-to-End Information Extraction by Character-Level Embedding and Multi-Stage Attentional U-Net

11 citations · 19 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2023

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

Shumpei Inoue, Minh-Tien Nguyen, Hiroki Mizokuchi +3

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 enti…

cs.IR2022

Improving Document Image Understanding with Reinforcement Finetuning

Bao-Sinh Nguyen, Dung Tien Le, Hieu M. Vu +3

Successful Artificial Intelligence systems often require numerous labeled data to extract information from document images. In this paper, we investigate the problem of improving t…

cs.IR2022

HYCEDIS: HYbrid Confidence Engine for Deep Document Intelligence System

Bao-Sinh Nguyen, Quang-Bach Tran, Tuan-Anh Nguyen Dang +2

Measuring the confidence of AI models is critical for safely deploying AI in real-world industrial systems. One important application of confidence measurement is information extra…

cs.CL2022

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

Nguyen Hong Son, Hieu M. Vu, Tuan-Anh D. Nguyen +1

This paper introduces a new information extraction model for business documents. Different from prior studies which only base on span extraction or sequence labeling, the model tak…

cs.AI2021

A Span Extraction Approach for Information Extraction on Visually-Rich Documents

Tuan-Anh D. Nguyen, Hieu M. Vu, Nguyen Hong Son +1

Information extraction (IE) for visually-rich documents (VRDs) has achieved SOTA performance recently thanks to the adaptation of Transformer-based language models, which shows the…

cs.AI2021★ 8 cited

End-to-End Hierarchical Relation Extraction for Generic Form Understanding

Tuan-Anh Nguyen Dang, Duc-Thanh Hoang, Quang-Bach Tran +2

Form understanding is a challenging problem which aims to recognize semantic entities from the input document and their hierarchical relations. Previous approaches face significant…