Structured Language Generation Model for Robust Structure Prediction
CoRR(2024)
摘要
We propose Structured Language Generation Model (SLGM), a mixture of new loss
function and inference method for better generalization of structured outputs.
Previous studies on structure prediction (e.g. NER, RE) make use of explicit
dataset information, which would boost performance, yet it might pose
challenges to robust generalization in real-world situations. Instead, our
model gives generalized format information about data indirectly. With format
information, we could reduce sequence-to-sequence problem into classification
problem via loss calibration and formatted decoding. Our experimental results
showed SLGM successfully maintain performance without dataset information, and
showed much less format errors. We also showed our model can work like adapters
on individual dataset, with no additional training.
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