Yi-shyuan Chiang


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FISH: A Financial Interactive System for Signal Highlighting
Ta-wei Huang | Jia-huei Ju | Yu-shiang Huang | Cheng-wei Lin | Yi-shyuan Chiang | Chuan-ju Wang
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

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.


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COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System
Manling Li | Revanth Gangi Reddy | Ziqi Wang | Yi-shyuan Chiang | Tuan Lai | Pengfei Yu | Zixuan Zhang | Heng Ji
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

To tackle the challenge of accurate and timely communication regarding the COVID-19 pandemic, we present a COVID-19 Claim Radar to automatically extract supporting and refuting claims on a daily basis. We provide a comprehensive structured view of claims, including rich claim attributes (such as claimers and claimer affiliations) and associated knowledge elements as claim semantics (such as events, relations and entities), enabling users to explore equivalent, refuting, or supporting claims with structural evidence, such as shared claimers, similar centroid events and arguments. In order to consolidate claim structures at the corpus-level, we leverage Wikidata as the hub to merge coreferential knowledge elements. The system automatically provides users a comprehensive exposure to COVID-19 related claims, their importance, and their interconnections. The system is publicly available at GitHub and DockerHub, with complete documentation.