a25193cc5d
This is lint reported by the new lint-checking functionality in beautify.py. (We can change to a different lint checker if we have a better one, but it would probably still flag these same problems.) Lint checking can help a lot, but only if we get the lint under control.
4.2 KiB
Vowpal Wabbit for Moses
This is an attempt to integrate Vowpal Wabbit with Moses as a stateless feature function.
Compatible with this frozen version of VW:
https://github.com/moses-smt/vowpal_wabbit
To enable VW, you need to provide a path where VW was installed (using make install) to bjam:
./bjam --with-vw=<path/to/vw/installation>
Implemented classifier features
VWFeatureSourceBagOfWords: This creates a feature of form bow^token for every source sentence token.VWFeatureSourceExternalFeatures column=0: when used with -inputtype 5 (TabbedSentence) this can be used to supply additional feature to VW. The input is a tab-separated file, the first column is the usual input sentence, all other columns can be used for meta-data. Parameter column=0 counts beginning with the first column that is not the input sentence.VWFeatureSourceIndicator: Ass a feature for the whole source phrase.VWFeatureSourcePhraseInternal: Adds a separate feature for every word of the source phrase.VWFeatureSourceWindow size=3: Adds source words in a window of size 3 before and after the source phrase as features. These do not overlap withVWFeatureSourcePhraseInternal.VWFeatureTargetIndicator: Adds a feature for the whole target phrase.VWFeatureTargetPhraseInternal: Adds a separate feature for every word of the target phrase.
Configuration
To use the classifier edit your moses.ini
[features]
...
VW path=/home/username/vw/classifier1.vw
VWFeatureSourceBagOfWords
VWFeatureTargetIndicator
VWFeatureSourceIndicator
...
[weights]
...
VW0= 0.2
...
If you change the name of the main VW feature, remember to tell the VW classifier features which classifier they belong to:
[features]
...
VW name=bart path=/home/username/vw/classifier1.vw
VWFeatureSourceBagOfWords used-by=bart
VWFeatureTargetIndicator used-by=bart
VWFeatureSourceIndicator used-by=bart
...
[weights]
...
bart= 0.2
...
You can also use multiple classifiers:
[features]
...
VW name=bart path=/home/username/vw/classifier1.vw
VW path=/home/username/vw/classifier2.vw
VW path=/home/username/vw/classifier3.vw
VWFeatureSourceBagOfWords used-by=bart,VW0
VWFeatureTargetIndicator used-by=VW1,VW0,bart
VWFeatureSourceIndicator used-by=bart,VW1
...
[weights]
...
bart= 0.2
VW0= 0.2
VW1= 0.2
...
Features can use any combination of factors. Provide a comma-delimited list of factors in the source-factors or target-factors variables to override the default setting (0, i.e. the first factor).
Training the classifier
Training uses vwtrainer which is a limited version of the moses binary. To train, provide your training data as input in the following format:
source tokens<tab>target tokens<tab>word alignment
Use Moses format for the word alignment (0-0 1-0 etc.). Set the input type to 5 (TabbedSentence, see above):
[inputtype]
5
Configure your features in the moses.ini file (see above) and set the train flag:
[features]
...
VW name=bart path=/home/username/vw/features.txt train=1
...
The path variable points to the file (prefix) where features will be written. Currently, threads write to separate files (maybe subject to change sooner or later): features.txt.1, features.txt.2 etc.
vwtrainer creates the translation option collection for each input sentence but does not run decoding. Therefore, you probably want to disable expensive feature functions such as the language model (LM score is not used by VW features at the moment).
Run vwtrainer:
vwtrainer -f moses.trainvw.ini < tab-separated-training-data.tsv
Currently, classification is implemented using VW's csoaa_ldf scheme with quadratic features which take the product of the source namespace (s, contains label-independent features) and the target namespace (t, contains label-dependent features).
To train VW in this setting, use the command:
cat features.txt.* | vw --hash all --loss_function logistic --noconstant -b 26 -q st --csoaa_ldf mc -f classifier1.vw