Papers by Yajing Wang
RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction (2022.findings-naacl)
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| Challenge: | Existing approaches to extract relations require large-scale labeled data. |
| Approach: | They propose a Relation Contrastive Learning framework to mitigate similar relations and similar entities problems by optimizing a contrastive instance loss with a relation classification loss on seen relations. |
| Outcome: | The proposed framework can learn subtle difference between instances and achieve better separation between different relation categories in the representation space simultaneously. |
Self-Guided Function Calling in Large Language Models via Stepwise Experience Recall (2025.findings-emnlp)
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| Challenge: | Existing methods for function calling require expert effort and prompt engineering becomes inefficient. |
| Approach: | They propose a method that performs fine-grained, stepwise retrieval from a continually updated experience pool. |
| Outcome: | The proposed method achieves an average improvement of 6.1% on easy and 4.7% on hard questions. |
Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration (2022.findings-naacl)
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| Challenge: | Existing methods to extract relational facts without pre-defined relation types cluster hard or semi-hard instances into the same relation type. |
| Approach: | They propose a method to learn discriminative representations for open relation extraction by using instance ranking and label calibration strategies. |
| Outcome: | The proposed method outperforms existing methods on two public datasets. |
StyleBART: Decorate Pretrained Model with Style Adapters for Unsupervised Stylistic Headline Generation (2023.findings-emnlp)
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| Challenge: | Existing studies on unsupervised headline generation focus on a standard dataset and mono-style corpora. |
| Approach: | They propose an unsupervised approach for stylistic headline generation using a pretrained BART model decorated with adapters responsible for different styles. |
| Outcome: | The proposed method separates the task of style learning and headline generation, allowing for the generation of diverse headlines with diverse styles. |
Cluster-aware Pseudo-Labeling for Supervised Open Relation Extraction (2022.coling-1)
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| Challenge: | Existing methods to extract novel relations do not achieve effective knowledge transfer . experimental results show that the proposed method is state-of-the-arts . |
| Approach: | They propose a Cluster-aware Pseudo-Labeling method to improve pseudo-labels quality . they firstly pre-trained the relation models with pre-defined relations to learn them . |
| Outcome: | The proposed method improves the pseudo-labels quality and transfer more knowledge for discovering novel relations. |