@inproceedings{huang-etal-2023-fish,
title = "{FISH}: A Financial Interactive System for Signal Highlighting",
author = "Huang, Ta-wei and
Ju, Jia-huei and
Huang, Yu-shiang and
Lin, Cheng-wei and
Chiang, Yi-shyuan and
Wang, Chuan-ju",
editor = "Croce, Danilo and
Soldaini, Luca",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-demo.7",
doi = "10.18653/v1/2023.eacl-demo.7",
pages = "50--56",
abstract = "In this system demonstration, we seek to streamline the process of reviewing financial statements and provide insightful information for practitioners. We develop FISH, an interactive system that extracts and highlights crucial textual signals from financial statements efficiently and precisely. To achieve our goal, we integrate pre-trained BERT representations and a fine-tuned BERT highlighting model with a newly-proposed two-stage classify-then-highlight pipeline. We also conduct the human evaluation, showing FISH can provide accurate financial signals. FISH overcomes the limitations of existing research andmore importantly benefits both academics and practitioners in finance as they can leverage state-of-the-art contextualized language models with their newly gained insights. The system is available online at \url{https://fish-web-fish.de.r.appspot.com/}, and a short video for introduction is at \url{https://youtu.be/ZbvZQ09i6aw}.",
}
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<abstract>In this system demonstration, we seek to streamline the process of reviewing financial statements and provide insightful information for practitioners. We develop FISH, an interactive system that extracts and highlights crucial textual signals from financial statements efficiently and precisely. To achieve our goal, we integrate pre-trained BERT representations and a fine-tuned BERT highlighting model with a newly-proposed two-stage classify-then-highlight pipeline. We also conduct the human evaluation, showing FISH can provide accurate financial signals. FISH overcomes the limitations of existing research andmore importantly benefits both academics and practitioners in finance as they can leverage state-of-the-art contextualized language models with their newly gained insights. The system is available online at https://fish-web-fish.de.r.appspot.com/, and a short video for introduction is at https://youtu.be/ZbvZQ09i6aw.</abstract>
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%0 Conference Proceedings
%T FISH: A Financial Interactive System for Signal Highlighting
%A Huang, Ta-wei
%A Ju, Jia-huei
%A Huang, Yu-shiang
%A Lin, Cheng-wei
%A Chiang, Yi-shyuan
%A Wang, Chuan-ju
%Y Croce, Danilo
%Y Soldaini, Luca
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F huang-etal-2023-fish
%X In this system demonstration, we seek to streamline the process of reviewing financial statements and provide insightful information for practitioners. We develop FISH, an interactive system that extracts and highlights crucial textual signals from financial statements efficiently and precisely. To achieve our goal, we integrate pre-trained BERT representations and a fine-tuned BERT highlighting model with a newly-proposed two-stage classify-then-highlight pipeline. We also conduct the human evaluation, showing FISH can provide accurate financial signals. FISH overcomes the limitations of existing research andmore importantly benefits both academics and practitioners in finance as they can leverage state-of-the-art contextualized language models with their newly gained insights. The system is available online at https://fish-web-fish.de.r.appspot.com/, and a short video for introduction is at https://youtu.be/ZbvZQ09i6aw.
%R 10.18653/v1/2023.eacl-demo.7
%U https://aclanthology.org/2023.eacl-demo.7
%U https://doi.org/10.18653/v1/2023.eacl-demo.7
%P 50-56
Markdown (Informal)
[FISH: A Financial Interactive System for Signal Highlighting](https://aclanthology.org/2023.eacl-demo.7) (Huang et al., EACL 2023)
ACL
- Ta-wei Huang, Jia-huei Ju, Yu-shiang Huang, Cheng-wei Lin, Yi-shyuan Chiang, and Chuan-ju Wang. 2023. FISH: A Financial Interactive System for Signal Highlighting. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 50–56, Dubrovnik, Croatia. Association for Computational Linguistics.