Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction
<|reference_start|>Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction: Predicting unseen relations that cannot be observed during the training phase is a challenging task in relation extraction. Previous works have made progress by matching the semantics between input instances and label descriptions. However, fine-grained matching often requires laborious manual annotation, and rich interactions between instances and label descriptions come with significant computational overhead. In this work, we propose an efficient multi-grained matching approach that uses virtual entity matching to reduce manual annotation cost, and fuses coarse-grained recall and fine-grained classification for rich interactions with guaranteed inference speed. Experimental results show that our approach outperforms the previous State Of The Art (SOTA) methods, and achieves a balance between inference efficiency and prediction accuracy in zero-shot relation extraction tasks. Our code is available at https://github.com/longls777/EMMA.<|reference_end|>
@article{li2024fusion,
    title={Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach
  for Zero-Shot Relation Extraction},
    author={Shilong Li, Ge Bai, Zhang Zhang, Ying Liu, Chenji Lu, Daichi Guo,
  Ruifang Liu, Yong Sun},
    journal={arXiv preprint arXiv:2406.11429},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11429},
    primaryClass={cs.CL cs.AI}
}