3 citations · 3 across the 6 of their papers we have counts for
6 papers
Asymmetric feature interaction for interpreting model predictions
Xiaolei Lu, Jianghong Ma, Haode Zhang
In natural language processing (NLP), deep neural networks (DNNs) could model complex interactions between context and have achieved impressive results on a range of NLP tasks. Pri…
Learning Ambiguity from Crowd Sequential Annotations
Xiaolei Lu
Most crowdsourcing learning methods treat disagreement between annotators as noisy labelings while inter-disagreement among experts is often a good indicator for the ambiguity and…
Modeling sequential annotations for sequence labeling with crowds
Xiaolei Lu, Tommy W. S. Chow
Crowd sequential annotations can be an efficient and cost-effective way to build large datasets for sequence labeling. Different from tagging independent instances, for crowd seque…
Weak Disambiguation for Partial Structured Output Learning
Xiaolei Lu, Tommy W. S. Chow
Existing disambiguation strategies for partial structured output learning just cannot generalize well to solve the problem that there are some candidates which can be false positiv…
Partial sequence labeling with structured Gaussian Processes
Xiaolei Lu, Tommy W. S. Chow
Existing partial sequence labeling models mainly focus on max-margin framework which fails to provide an uncertainty estimation of the prediction. Further, the unique ground truth…
Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization
Haode Zhang, Haowen Liang, Yuwei Zhang +4
It is challenging to train a good intent classifier for a task-oriented dialogue system with only a few annotations. Recent studies have shown that fine-tuning pre-trained language…