Papers by Zekai Wang
Unsupervised Sentence Representation Learning with Syntactically Aligned Negative Samples (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to sentence representation learning often encounter semantic inconsistencies and feature suppression. |
| Approach: | They propose a method for generating syntactically aligned negative (SAN) samples using a semantic importance-aware Masked Language Model (MLM) approach. |
| Outcome: | The proposed method produces negative samples with substantial textual overlap with the original sentences while conveying different meanings. |
BanditMTL: Bandit-based Multi-task Learning for Text Classification (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to regularize task variance are unexplored in multi-task text classification. |
| Approach: | They propose a multi-task learning method based on adversarial multi-armed bandit to regularize the task variance by means of a mirror gradient ascent-descent algorithm. |
| Outcome: | The proposed method achieves state-of-the-art in multi-task text classification. |
MetaWeighting: Learning to Weight Tasks in Multi-Task Learning (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing task weighting methods assign weights only based on training losses, while ignoring the gap between the training loss and generalization loss. |
| Approach: | They propose a task weighting algorithm which automatically weights the tasks via a learning-to-learn paradigm and a multi-task text classification paradigm. |
| Outcome: | Extensive experiments show that the proposed method outperforms existing methods in multi-task text classification. |