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# Semantic parsing as machine translation
Most work on semantic parsing, even in variable-free formulations, has focused
on developing task- and formalism-specific models, often with expensive
training and decoding procedures. Can we use standard machine translation tools
to perform the same task?
Yes.
For a description of the system (it's really not complicated), see:
- J Andreas, A Vlachos and S Clark. "Semantic Parsing as Machine
Translation". To appear in ACL-SHORT 2013.
### Getting started
Edit `dependencies.yaml` to reflect the configuration of your system.
`smt_semparse` should be set to the location of the repository root, the
`moses`, `srilm`, etc. entries to the roots of the corresponding external
dependencies, and `srilm_arch` to your machine architecture.
### Reproducing the ACL13 paper
Edit settings.yaml to choose a language and translation model for the particular
experiment you want to run. Use the following additional settings:
lang=en -> stem=true, symm=srctotgt
lang=de -> stem=true, symm=tgttosrc
lang=el -> stem=false, symm=tgttosrc
lang=th -> stem=false, symm=tgttosrc
Note that due to random MERT initialization your exact accuracy and F1 values
may differ slightly from those in the paper.
### Experimental things
Additional settings also allow you to do the following:
- Rebuild the phrase table after running MERT to squeeze a few more translation
rules out of the training data. (Should give a nearly-imperceptible
improvement in accuracy.)
- Filter rules which correspond to multi-rooted forests from the phrase table.
(Should decrease accuracy.)
- Do full-supervised training on only a fraction of the dataset, and use the
remaining monolingual data to reweight rules. (Mostly garbage---this data set
is already too small to permit experiments which require holding out even more
data.)
### Not implemented
MRL-to-NL à la Lu & Ng 2011.
### Using a new dataset
Update `extractor.py` to create appropriately-formatted files in the working
directory. See the existing GeoQuery extractor for an example.
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