Papers by Tse-Hsuan Yang
Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to extract rationales from input text are difficult and impractical. |
| Approach: | They propose a method that leverages multi-task learning and transfer learning to generate rationales through question answering in a zero-shot fashion. |
| Outcome: | The proposed method achieves comparable or even better performance without supervised signal for two benchmark rationalization datasets. |
Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity (2021.emnlp-main)
Copied to clipboard
| Challenge: | Multi-task auxiliary learning uses a set of relevant auxiliary tasks to improve performance of a primary task. |
| Approach: | They propose a time-efficient sampling method to select the most beneficial sub-datasets from the auxiliary tasks to achieve efficient multi-task auxiliary learning. |
| Outcome: | The proposed method significantly outperforms random sampling and ST-DNN on three benchmark datasets. |