Papers by Yunlong Jiao
Rethinking Semi-supervised Learning with Language Models (2023.findings-acl)
Copied to clipboard
| Challenge: | Semi-supervised learning (SSL) is a popular setting to make use of unlabelled data . Currently, there are two popular approaches to make effective use of the unlabelled datasets . |
| Approach: | They compare semi-supervised learning (SSL) and task-adaptive pre-training (TAPT) they find TAPT is a stronger and more robust SSL learner, even when using just a few hundred unlabelled samples . |
| Outcome: | The proposed methods improve model performance across different NLP tasks and data sizes. |
Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on USM neglect explicitly modeling the user’s task goals fulfillment using the task schema. |
| Approach: | They propose a schema-guided user satisfaction modeling framework that explicitly models the degree to which the user’s preferences regarding task attributes are fulfilled by the system. |
| Outcome: | The proposed framework outperforms existing methods on benchmark datasets and shows that it can interpret and scale well with unseen tasks and can work in low-resource settings. |