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The explosion in the sheer magnitude and complexity of financial news data in recent years makes it increasingly challenging for investment analysts to extract valuable insights and perform analysis.
Using structured events to predict stock price movement: An empirical investigation
Xiao Ding, Yue Zhang, Ting Liu, and Junwen Duan · 2014
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Unsupervised event clustering and aggregation from newswire and web articles
Swen Ribeiro, Olivier Ferret, and Xavier Tannier · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Explainable text-driven neural network for stock prediction
Linyi Yang, Zheng Zhang, Su Xiong, Lirui Wei, James Ng, Lina Xu, and Ruihai Dong · 2018
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Tanbih: Get to know what you are reading
Yifan Zhang, Giovanni Da San Martino, Alberto Barrón-Cedeno, Salvatore Romeo, Jisun An, Haewoon Kwak, Todor Staykovski, Israa Jaradat, Georgi Karadzhov, Ramy Baly, et al · 2019
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Generating plausible counterfactual explanations for deep transformers in financial text classification
Linyi Yang, Eoin Kenny, Tin Lok James Ng, Yi Yang, Barry Smyth, and Ruihai Dong · 2020
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Maec: A multimodal aligned earnings conference call dataset for financial risk prediction
Linyi Yang, Jiazheng Li, Barry Smyth, and Ruihai Dong · 2020
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Html: Hierarchical transformer-based multi-task learning for volatility prediction
Linyi Yang, Tin Lok James Ng, Barry Smyth, and Ruihai Dong · 2020
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Exploring the efficacy of automatically generated counterfactuals for sentiment analysis
Linyi Yang, Jiazheng Li, Pádraig Cunningham, Yue Zhang, Barry Smyth, and Ruihai Dong · 2021
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Usb: A unified semi-supervised learning benchmark for classification
Yidong Wang, Hao Chen, Yue Fan, Wang Sun, Ran Tao, Wenxin Hou, Renjie Wang, Linyi Yang, Zhi Zhou, Lan-Zhe Guo, et al · 2022
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Numhtml: Numeric-oriented hierarchical transformer model for multi-task financial forecasting
Linyi Yang, Jiazheng Li, Ruihai Dong, Yue Zhang, and Barry Smyth · 2022
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A rationale-centric framework for human-in-the-loop machine learning
Linyi Yang, Jinghui Lu, Brian Mac Namee, and Yue Zhang · 2022
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Learning to generalize for cross-domain qa
Yingjie Niu, Linyi Yang, Ruihai Dong, and Yue Zhang · 2023
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On the robustness of chatgpt: An adversarial and out-of-distribution perspective
Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, et al · 2023
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Pandalm: An automatic evaluation benchmark for llm instruction tuning optimization
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, et al · 2023
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Glue-x: Evaluating natural language understanding models from an out-of-distribution generalization perspective
Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, and Yue Zhang · 2023
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Pandalm: Reproducible and automated language model assessment, 2023
Wang Yidong, Yu Zhuohao, Zeng Zhengran, Yang Linyi, Heng Qiang, Wang Cunxiang, Chen Hao, Jiang Chaoya, Xie Rui, Wang Jindong, et al · 2023
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Factmix: Using a few labeled in-domain examples to generalize to cross-domain named entity recognition
Linyi Yang, Lifan Yuan, Leyang Cui, Wenyang Gao, and Yue Zhang · 2022
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A survey on evaluation of large language models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Kaijie Zhu, Hao Chen, Linyi Yang, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al · 2023
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Promptbench: Towards evaluating the robustness of large language models on adversarial prompts
Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Neil Zhenqiang Gong, Yue Zhang, et al · 2023
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