ACL 2013.

### Abstract

Given that structured output prediction is typically performed
over entire datasets, one natural question is whether it is
possible to re-use computation from earlier inference instances to
speed up inference for future instances. Amortized inference has
been proposed as a way to accomplish this. In this paper, first,
we introduce a new amortized inference algorithm called
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.

### Links

- Link to paper
- This paper on Google Scholar

### Bib Entry

@inproceedings{KunduSrRo13, author = {G. Kundu and V. Srikumar and D. Roth}, title = {Margin-based Decomposed Amortized Inference}, booktitle = {ACL}, month = {8}, year = {2013}, }