19 citations · 47 across the 8 of their papers we have counts for
8 papers
UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification
Yanbo Xu, Alind Khare, Glenn Matlin +4
Machine Learning (ML) research has focused on maximizing the accuracy of predictive tasks. ML models, however, are increasingly more complex, resource intensive, and costlier to de…
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select
Yuchen Zhuang, Yinghao Li, Jerry Junyang Cheung +5
We study the problem of extracting N-ary relation tuples from scientific articles. This task is challenging because the target knowledge tuples can reside in multiple parts and mod…
SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling
Mengyang Liu, Haozheng Luo, Leonard Thong +3
Weak labeling is a popular weak supervision strategy for Named Entity Recognition (NER) tasks, with the goal of reducing the necessity for hand-crafted annotations. Although there…
Learning Disentangled Representations for Controllable Human Motion Prediction
Chunzhi Gu, Jun Yu, Chao Zhang
Generative model-based motion prediction techniques have recently realized predicting controlled human motions, such as predicting multiple upper human body motions with similar lo…
Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction
Rongzhi Zhang, Rebecca West, Xiquan Cui +1
On e-commerce platforms, predicting if two products are compatible with each other is an important functionality to achieve trustworthy product recommendation and search experience…
Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition
Yinghao Li, Le Song, Chao Zhang
Weakly supervised named entity recognition methods train label models to aggregate the token annotations of multiple noisy labeling functions (LFs) without seeing any manually anno…
PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning
Rongzhi Zhang, Yue Yu, Pranav Shetty +2
Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set…
Probing and Fine-tuning Reading Comprehension Models for Few-shot Event Extraction
Rui Feng, Jie Yuan, Chao Zhang
We study the problem of event extraction from text data, which requires both detecting target event types and their arguments. Typically, both the event detection and argument dete…