Papers by Zechuan Hu
Risk Minimization for Zero-shot Sequence Labeling (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to zero-shot sequence labeling are expensive and hard to obtain for lowresource languages/domains. |
| Approach: | They propose a framework for zero-shot sequence labeling with minimum risk training and a decomposable risk function that models the relations between predicted labels from the source models and the true labels. |
| Outcome: | The proposed framework outperforms state-of-the-art systems on 21 datasets. |
Multi-View Cross-Lingual Structured Prediction with Minimum Supervision (2021.acl-long)
Copied to clipboard
| Challenge: | Existing work on cross-lingual transfer learning focuses on transferring knowledge from high-resource languages to low-resourced ones. |
| Approach: | They propose a multi-view framework that integrates multiple source models into an aggregated source view and transfers it to a target view based on a task-specific model. |
| Outcome: | The proposed framework improves on three structured prediction tasks on 16 datasets. |
An Investigation of Potential Function Designs for Neural CRF (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to sequence labeling are based on the neural linear-chain CRF model. |
| Approach: | They propose a series of increasingly expressive potential functions for neural CRF models that integrate emission and transition functions and explicitly take contextual words as input. |
| Outcome: | The proposed model consistently achieves the best performance on the decomposed quadrilinear potential function based on the representations of two neighboring labels and two neighbored words. |