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In this paper, first, we introduce a new amortized inference algorithm called the "Margin-based Amortized Inference", which uses the notion of structured margin to identify inference problems for which previous solutions are provably optimal.
Second, we introduce "decomposed amortized inference", which is designed to address very large inference problems, where earlier amortization methods become less effective.
This approach works by decomposing the output structure and applying amortization piece-wise, thus increasing the chance that we can re-use previous solutions for "parts" of the output structure. These parts are then combined to a global coherent solution using Lagrangian relaxation. In our experiments, using the NLP tasks of semantic role labeling and entity-relation extraction, we demonstrate that with the margin-based algorithm, we need to call the inference engine only for a third of the test examples. Further, we show that the decomposed variant of margin-based amortized inference achieves a greater reduction in the number of inference calls.
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