Chuan-Ju Wang


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Designing Templates for Eliciting Commonsense Knowledge from Pretrained Sequence-to-Sequence Models
Jheng-Hong Yang | Sheng-Chieh Lin | Rodrigo Nogueira | Ming-Feng Tsai | Chuan-Ju Wang | Jimmy Lin
Proceedings of the 28th International Conference on Computational Linguistics

While internalized “implicit knowledge” in pretrained transformers has led to fruitful progress in many natural language understanding tasks, how to most effectively elicit such knowledge remains an open question. Based on the text-to-text transfer transformer (T5) model, this work explores a template-based approach to extract implicit knowledge for commonsense reasoning on multiple-choice (MC) question answering tasks. Experiments on three representative MC datasets show the surprisingly good performance of our simple template, coupled with a logit normalization technique for disambiguation. Furthermore, we verify that our proposed template can be easily extended to other MC tasks with contexts such as supporting facts in open-book question answering settings. Starting from the MC task, this work initiates further research to find generic natural language templates that can effectively leverage stored knowledge in pretrained models.


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RiskFinder: A Sentence-level Risk Detector for Financial Reports
Yu-Wen Liu | Liang-Chih Liu | Chuan-Ju Wang | Ming-Feng Tsai
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

This paper presents a web-based information system, RiskFinder, for facilitating the analyses of soft and hard information in financial reports. In particular, the system broadens the analyses from the word level to sentence level, which makes the system useful for practitioner communities and unprecedented among financial academics. The proposed system has four main components: 1) a Form 10-K risk-sentiment dataset, consisting of a set of risk-labeled financial sentences and pre-trained sentence embeddings; 2) metadata, including basic information on each company that published the Form 10-K financial report as well as several relevant financial measures; 3) an interface that highlights risk-related sentences in the financial reports based on the latest sentence embedding techniques; 4) a visualization of financial time-series data for a corresponding company. This paper also conducts some case studies to showcase that the system can be of great help in capturing valuable insight within large amounts of textual information. The system is now online available at


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Financial Keyword Expansion via Continuous Word Vector Representations
Ming-Feng Tsai | Chuan-Ju Wang
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


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Financial Sentiment Analysis for Risk Prediction
Chuan-Ju Wang | Ming-Feng Tsai | Tse Liu | Chin-Ting Chang
Proceedings of the Sixth International Joint Conference on Natural Language Processing


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Visualization on Financial Terms via Risk Ranking from Financial Reports
Ming-Feng Tsai | Chuan-Ju Wang
Proceedings of COLING 2012: Demonstration Papers