HIVE: Hierarchical Information Visualization for Explainability
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Demo Track. Pages 4988-4991.
https://doi.org/10.24963/ijcai.2021/709
Keywords
- Natural Language Processing: General
Abstract
In this demonstration, we develop an interactive tool, HIVE, to demonstrate the ability and versatility of an explainable risk ranking model with a special focus on financial use cases. HIVE is a web-based tool that provides users with automated highlighted financial statements, and HIVE is designed for making comparing statements rather more efficient. Moreover, with the proposed tool, users can find related reports at ease, and we believe that HIVE can benefit both academics and practitioners in finance as they can work around deep learning models with their newly gained insights.
@inproceedings{ijcai2021p709,
title = {HIVE: Hierarchical Information Visualization for Explainability},
author = {Juan, Yi-Ning and Chiang, Yi-Shyuan and Liu, Shang-Chuan and Tsai, Ming-Feng and Wang, Chuan-Ju},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Prof. Zhi-Hua Zhou},
pages = {4988--4991},
year = {2021},
month = {8},
note = {Demo Track},
doi = {10.24963/ijcai.2021/709},
url = {https://doi.org/10.24963/ijcai.2021/709},
}
TY - CPAPER TI - HIVE: Hierarchical Information Visualization for Explainability AU - Juan, Yi-Ning AU - Chiang, Yi-Shyuan AU - Liu, Shang-Chuan AU - Tsai, Ming-Feng AU - Wang, Chuan-Ju T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2021 SP - 4988 EP - 4991 DO - 10.24963/ijcai.2021/709 UR - https://doi.org/10.24963/ijcai.2021/709 ER -Copied
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