Papers by Shaojuan Wu
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods to zero-shot transfer knowledge from rich-resource to low-resourced languages are limited due to linguistic discrepancies in different languages. |
| Approach: | They propose a multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model to disassociate semantics from syntax in models learned by multilingual pre-trained models. |
| Outcome: | The proposed model disassociates semantics from syntax in multilingual models. |
Causal Intervention for Mitigating Name Bias in Machine Reading Comprehension (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing MRC models may overuse name information to make predictions, causing name bias . |
| Approach: | They propose a Causal Interventional paradigm for MRC to mitigate name bias by analyzing pre-trained knowledge and context representations. |
| Outcome: | The proposed model is robust to names and performs competitively on the original SQuAD. |
Re-embedding Difficult Samples via Mutual Information Constrained Semantically Oversampling for Imbalanced Text Classification (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing frameworks for imbalanced text classification can generate anchor instances for difficult samples . difficult samples are hard to classify as they are embedded into an overlapping semantic region with the majority class. |
| Approach: | They propose a Mutual Information constrained Semantically Oversampling framework that generates anchor instances for difficult samples to help the backbone network determine the re-embedding position of a non-overlapping representation. |
| Outcome: | The proposed framework can generate anchor instances to help classifiers achieve significant improvements over baselines on a variety of imbalanced text classification tasks. |
An Event-based Abductive Learning for Hard Time-sensitive Question Answering (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing time-sensitive question answering models are limited for hard time-sensitive questions whose time qualifiers are implicit in the document. |
| Approach: | They propose a time-sensitive question answering framework that matches temporal events in documents with time qualifiers. |
| Outcome: | The proposed model outperforms baseline models for hard time-sensitive questions with 12.7% improvement in EM scores. |