  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.
