Wed 6 Dec 2023 14:30 - 14:45 at Golden Gate C2 - Machine Learning III Chair(s): Rangeet Pan

Named entity recognition (NER) systems have seen rapid progress in recent years due to the development of deep neural networks. These systems are widely used in various natural language processing applications, such as information extraction, question answering, and sentiment analysis. However, the complexity and intractability of deep neural networks can make NER systems unreliable in certain circumstances, resulting in incorrect predictions. For example, NER systems may misidentify female names as chemicals or fail to recognize the names of minority groups, leading to user dissatisfaction. To tackle this problem, we introduce TIN, a novel, widely applicable approach for automatically testing and repairing various NER systems. The key idea for automated testing is that the NER predictions of the same named entities under similar contexts should be identical. The core idea for automated repairing is that similar named entities should have the same NER prediction under the same context. We use TIN to test two SOTA NER models and two commercial NER APIs, i.e., Azure NER and AWS NER. We manually verify 782 of the suspicious issues reported by TIN and find that 702 are erroneous issues, leading to high precision (85.0%-93.4%) across four categories of NER errors: omission, over-labeling, incorrect category, and range error. For automated repairing, TIN achieves a high error reduction rate (26.8%-50.6%) over the four systems under test, which successfully repairs 1,056 out of the 1,877 reported NER errors.

Wed 6 Dec

Displayed time zone: Pacific Time (US & Canada) change

14:00 - 15:30
Machine Learning IIIDemonstrations / Industry Papers / Research Papers at Golden Gate C2
Chair(s): Rangeet Pan IBM Research
14:00
15m
Talk
Benchmarking Robustness of AI-enabled Multi-sensor Fusion Systems: Challenges and Opportunities
Research Papers
Xinyu Gao , Zhijie Wang University of Alberta, Yang Feng Nanjing University, Lei Ma The University of Tokyo / University of Alberta, Zhenyu Chen Nanjing University, Baowen Xu Nanjing University
Media Attached
14:15
7m
Talk
A Language Model of Java Methods with Train/Test Deduplication
Demonstrations
Chia-Yi Su University of Notre Dame, Aakash Bansal University of Notre Dame, Vijayanta Jain University of Maine, Sepideh Ghanavati University of Maine , Collin McMillan University of Notre Dame
Media Attached
14:23
7m
Talk
DENT - A Tool for Tagging Stack Overflow Posts With Deep Learning Energy Patterns
Demonstrations
Shriram Shanbhag Indian Institute of Technology Tirupati, Sridhar Chimalakonda Indian Institute of Technology Tirupati, Vibhu Saujanya Sharma Accenture Labs, India, Vikrant Kaulgud Accenture Labs, India
Media Attached
14:30
15m
Talk
Automated Testing and Improvement of Named Entity Recognition Systems
Research Papers
BoXi Yu The Chinese University of Hong Kong, Shenzhen, Yiyan Hu The Chinese University of Hong Kong, Shenzhen, Qiuyang Mang The Chinese University of Hong Kong, Shenzhen, Wenhan Hu The Chinese University of Hong Kong, Shenzhen, Pinjia He The Chinese University of Hong Kong, Shenzhen
Pre-print Media Attached
14:45
15m
Talk
KDDT: Knowledge Distillation-Empowered Digital Twin for Anomaly Detection
Industry Papers
Xu Qinghua Simula Research Laboratory; University of Oslo, Shaukat Ali Simula Research Laboratory and Oslo Metropolitan University, Tao Yue Beihang University, Zaimovic Nedim Alstom Rail, Inderjeet Singh Alstom
DOI Media Attached
15:00
15m
Talk
Deep Learning Based Feature Envy Detection Boosted by Real-World Examples
Research Papers
Bo Liu Beijing Institute of Technology, Hui Liu Beijing Institute of Technology, Guangjie Li National Innovation Institute of Defense Technology, Nan Niu University of Cincinnati, Zimao Xu Beijing Institute of Technology, Yifan Wang Huawei Cloud, Yunni Xia Chongqing University, Yuxia Zhang Beijing Institute of Technology, Yanjie Jiang Peking University
DOI Pre-print Media Attached
15:15
15m
Talk
[Remote] The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification
Research Papers
Anastasiia Grishina Simula Research Laboratory, Max Hort Simula Research Laboratory, Leon Moonen Simula Research Laboratory and BI Norwegian Business School
Pre-print Media Attached