Papers by Aysa Xuemo Fan
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction (2020.findings-emnlp)
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Ranran Haoran Zhang, Qianying Liu, Aysa Xuemo Fan, Heng Ji, Daojian Zeng, Fei Cheng, Daisuke Kawahara, Sadao Kurohashi
| Challenge: | Existing methods to extract relation triplets from plain text introduce exposure bias . prior work has focused on pipeline methods that ignore intrinsic interactions between subtasks and propagate classification errors through the tasks. |
| Approach: | They propose a model that reduces the decoding length to three within a triplet and removes the order among triplets. |
| Outcome: | The proposed model overfits to both datasets while showing better generalization. |
ConEntail: An Entailment-based Framework for Universal Zero and Few Shot Classification with Supervised Contrastive Pretraining (2023.eacl-main)
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| Challenge: | Existing models for text classification are not universally applicable and lack annotated data. |
| Approach: | They propose a framework for universal zero and few shot classification with supervised contrastive pretraining that can generalize to diverse classification tasks in both zero and many shot settings. |
| Outcome: | The proposed framework outperforms baseline models in zero and few shot settings. |