Structured Language Generation Model for Robust Structure Prediction
<|reference_start|>Structured Language Generation Model for Robust Structure Prediction: Previous work in structured prediction (e.g. NER, information extraction) using single model make use of explicit dataset information, which helps boost in-distribution performance but is orthogonal to robust generalization in real-world situations. To overcome this limitation, we propose the Structured Language Generation Model (SLGM), a framework that reduces sequence-to-sequence problems to classification problems via methodologies in loss calibration and decoding method. Our experimental results show that SLGM is able to maintain performance without explicit dataset information, follow and potentially replace dataset-specific fine-tuning.<|reference_end|>
@article{lee2024structured,
    title={Structured Language Generation Model for Robust Structure Prediction},
    author={Minho Lee and Junghyun Min and Woochul Lee and Yeonsoo Lee},
    journal={arXiv preprint arXiv:2402.08971},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08971},
    primaryClass={cs.CL}
}