Structured Language Generation Model for Robust Structure Prediction
This addresses the issue of robust generalization for real-world applications in structured prediction, though it appears incremental as it builds on existing methodologies.
The paper tackled the problem of robust generalization in structured prediction tasks like NER and information extraction, proposing the Structured Language Generation Model (SLGM) to reduce sequence-to-sequence problems to classification via loss calibration and decoding, which maintains performance without explicit dataset information and can replace dataset-specific fine-tuning.
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.