Published in WWW 2018 (The Web Conf 2018) in Lyon, 2018
Extract formulas from text using deep learning method.
Recommended citation: Yixuan Cao, Hongwei Li, Ping Luo, and Jiaquan Yao. Towards Automatic Numerical Cross-Checking: Extracting Formulas from Text. In WWW, 2018. https://dl.acm.org/doi/pdf/10.1145/3178876.3186166
Published in CIKM, 2019
nested relation extraction with iterative neural network
Recommended citation: Yixuan Cao, Dian Chen, Hongwei Li, and Ping Luo. Nested Relation Extraction with Iterative Neural Network. In CIKM, 2019. https://dl.acm.org/doi/abs/10.1145/3357384.3358003
Published in KDD, 2020
automatic cross-checking over numerical facts in tables
Recommended citation: Hongwei Li, Qingping Yang, Yixuan Cao, Jiaquan Yao, and Ping Luo. Cracking Tabular Presentation Diversity for Automatic Cross-Checking over Numerical Facts. In KDD, 2020. https://dl.acm.org/doi/abs/10.1145/3394486.3403310
Published in AAAI, 2020
solve math word problem as extracting a DAG structure upon text
Recommended citation: Yixuan Cao, Feng Hong, Hongwei Li, and Ping Luo. A Bottom-Up DAG Structure Extraction Model for Math Word Problems. In AAAI, 2021. https://doi.org/10.1609/aaai.v35i1.16075
Published in KDD, 2021
extracting numerical calculation formulas in tables
Recommended citation: Qingping Yang, Yixuan Cao, Hongwei Li, and Ping Luo. Numerical Formula Recognition from Tables. In KDD, 2021. https://dl.acm.org/doi/abs/10.1145/3447548.3467425
Published in CIKM, 2021
Assess compliance for annual reports of listed company against hundreds of listing rules
Recommended citation: Zhengqi Xu, Yixuan Cao, et al. Numerical Formula Recognition from Tables. In CIKM, 2021. https://dl.acm.org/doi/abs/10.1145/3459637.3481929
Published in JCST, 2021
We study the problem of extracting variable-depth “logical document hierarchy”, i.e. table of contents, from long documents.
Recommended citation: Rongyu Cao, Yixuan Cao, Ganbin Zhou, and Ping Luo. Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application. Journal of Computer Science and Technology, 2021. https://doi.org/10.1007/s11390-021-1076-7
Published in KDD, 2022
Extracting numerical tuples from tables considering both Hierarchy and Juxtaposition using a BERT-based language model TableLM
Recommended citation: Qingping Yang, Yixuan Cao, Yinging Hu, Jianfeng Li, Nanbo Peng, and Ping Luo. Numerical Tuple Extraction from Tables with Pre-training. In KDD, 2022. https://dl.acm.org/doi/abs/10.1145/3534678.3539460
Published in NeurIPS, 2023
We prove that, under some assumptions, ensemble model outperforms its members on a range of coverage for selective classification.
Recommended citation: Qiang Ding, Yixuan Cao, and Ping Luo. Top-Ambiguity Samples Matter: Understanding Why Deep Ensemble Works in Selective Classification. In NeurIPS, 2023. https://openreview.net/forum?id=WBq6Q4ml04
Published in EMNLP, 2023
We propose to learn the task description (guideline) to learn from training samples for in-context learning
Recommended citation: Chaoxu Pang, Yixuan Cao, Qiang Ding, and Ping Luo. 2023. Guideline Learning for In-Context Information Extraction. In EMNLP, 2023. https://aclanthology.org/2023.emnlp-main.950/