Cheng-Wei Lin

Also published as: Cheng-wei Lin


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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, and a short video for introduction is at

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A Compare-and-contrast Multistage Pipeline for Uncovering Financial Signals in Financial Reports
Jia-Huei Ju | Yu-Shiang Huang | Cheng-Wei Lin | Che Lin | Chuan-Ju Wang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we address the challenge of discovering financial signals in narrative financial reports. As these documents are often lengthy and tend to blend routine information with new information, it is challenging for professionals to discern critical financial signals. To this end, we leverage the inherent nature of the year-to-year structure of reports to define a novel signal-highlighting task; more importantly, we propose a compare-and-contrast multistage pipeline that recognizes different relationships between the reports and locates relevant rationales for these relationships. We also create and publicly release a human-annotated dataset for our task. Our experiments on the dataset validate the effectiveness of our pipeline, and we provide detailed analyses and ablation studies to support our findings.