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#!/usr/bin/env ruby
# This is the main entry point for running all SEMPRE programs. See
# fig/lib/execrunner.rb for more documentation for how commands are generated.
# There are a bunch of modes that this script can be invoked with, which
# loosely correspond to the modules.
$: << 'fig/lib'
require 'execrunner'
$modes = []
def addMode(name, description, func)
$modes << [name, description, func]
end
def codalab(dependencies=nil)
# Set @cl=1 to run job on CodaLab
dependencies ||= l(':fig', ':lib', ':module-classes.txt', ':libsempre')
l(
letDefault(:cl, 0),
sel(:cl,
l(),
l('cl', 'run', dependencies, '---', 'LC_ALL=C.UTF-8'),
nil),
nil)
end
def header(modules='core', codalabDependencies=nil)
l(
codalab(codalabDependencies),
# Queuing system
letDefault(:q, 0), sel(:q, l(), l('fig/bin/q', '-shareWorkingPath', o('mem', '5g'), o('memGrace', 10), '-add', '---')),
# Create execution directory
letDefault(:pooldir, 1),
sel(:pooldir, l(), 'fig/bin/qcreate'),
# Run the Java command...
'java',
'-ea',
'-Dmodules='+modules,
# Memory size
letDefault(:memsize, 'default'),
sel(:memsize, {
'tiny' => l('-Xms2G', '-Xmx4G'),
'low' => l('-Xms5G', '-Xmx7G'),
'default' => l('-Xms8G', '-Xmx10G'),
'medium' => l('-Xms12G', '-Xmx14G'),
'high' => l('-Xms20G', '-Xmx24G'),
'higher' => l('-Xms40G', '-Xmx50G'),
'impressive' => l('-Xms75G', '-Xmx90G'),
}),
# Classpath
'-cp', 'libsempre/*:lib/*',
# Profiling
letDefault(:prof, 0), sel(:prof, l(), '-Xrunhprof:cpu=samples,depth=100,file=_OUTPATH_/java.hprof.txt'),
nil)
end
def unbalancedTrainDevSplit
l(o('Dataset.trainFrac', 0.8), o('Dataset.devFrac', 0.2))
end
def balancedTrainDevSplit
l(o('Dataset.trainFrac', 0.5), o('Dataset.devFrac', 0.5))
end
def figOpts; l(selo(:pooldir, 'execDir', 'exec', '_OUTPATH_'), o('overwriteExecDir'), o('addToView', 0)) end
############################################################
# Unit tests
addMode('test', 'Run unit tests', lambda { |e|
l(
'java', '-ea', '-Xmx12g', '-cp', 'libsempre/*:lib/*',
lambda { |e|
e.key?(:sparqlserver) ? "-Dsparqlserver=http://#{e[:sparqlserver]}/sparql" : l()
},
'org.testng.TestNG',
lambda { |e|
if e[:class]
l('-testclass', 'edu.stanford.nlp.sempre.' + e[:class])
else
'testng.xml'
end
},
lambda { |e|
if e[:fast]
o('excludegroups', 'sparql,corenlp')
else
nil
end
},
nil)
})
############################################################
# Freebase
def freebaseHeader; header('core,freebase') end
def freebaseFeatureDomains
[
'basicStats',
'alignmentScores',
'entityFeatures',
'context',
'skipPos',
'joinPos',
'wordSim',
'lexAlign',
'tokenMatch',
'rule',
'opCount',
'constant',
'denotation',
'whType',
'span',
'derivRank',
'lemmaAndBinaries',
nil].compact
end
def sparqlOpts
l(
required(:sparqlserver, 'host:port of the Sparql server'), # Example: jonsson:3093, etc.
o('SparqlExecutor.endpointUrl', lambda{|e| 'http://'+e[:sparqlserver]+'/sparql'}),
nil)
end
def freebaseOpts
l(
figOpts,
sparqlOpts,
# Features
o('FeatureExtractor.featureDomains', *freebaseFeatureDomains),
o('Builder.executor', 'freebase.SparqlExecutor'),
o('Builder.valueEvaluator', 'freebase.FreebaseValueEvaluator'),
o('LanguageAnalyzer.languageAnalyzer', 'corenlp.CoreNLPAnalyzer'),
# Lexicon
o('LexiconFn.lexiconClassName', 'edu.stanford.nlp.sempre.fbalignment.lexicons.Lexicon'),
l( # binary
o('BinaryLexicon.binaryLexiconFilesPath', 'lib/fb_data/7/binaryInfoStringAndAlignment.txt'),
o('BinaryLexicon.keyToSortBy', 'Intersection_size_typed'),
nil),
o('UnaryLexicon.unaryLexiconFilePath','lib/fb_data/7/unaryInfoStringAndAlignment.txt'), # unary
o('EntityLexicon.entityPopularityPath','lib/fb_data/7/entityPopularity.txt'), # entity
#Jonathan - added this 3/5/2015
o('TypeInference.typeLookup','freebase.FreebaseTypeLookup'),
o('FreebaseSearch.cachePath', '/u/nlp/data/semparse/scr/cache/fbsearch/1.cache'),
nil)
end
def cachePaths(lexiconFnCachePath, sparqlExecutorCachePath)
l(
required(:cacheserver, 'none (don\'t cache to disk), local (write to local file), or <hostname>:<port> (hit the cacheserver)'),
lambda { |e|
cacheserver = e[:cacheserver]
cacheserver = 'jonsson:4000' if cacheserver == 'remote' # Default
case cacheserver
when 'none' then l()
when 'local' then l( # Use files directly - don't run more than one job that does this!
o('Lexicon.cachePath', 'LexiconFn.cache'),
o('SparqlExecutor.cachePath', 'SparqlExecutor.cache'),
o('FreebaseSearch.cachePath', 'FreebaseSearch.cache'),
nil)
else l(
o('Lexicon.cachePath', cacheserver+':/u/nlp/data/semparse/cache/'+lexiconFnCachePath),
o('SparqlExecutor.cachePath', cacheserver+':/u/nlp/data/semparse/cache/'+sparqlExecutorCachePath),
o('FreebaseSearch.cachePath', cacheserver+':/u/nlp/data/semparse/cache/fbsearch/1.cache'),
# Read-only
o('EntityLexicon.mid2idPath', cacheserver+':/u/nlp/data/semparse/scr/freebase/freebase-rdf-2013-06-09-00-00.canonical-id-map'),
o('FreebaseTypeLookup.entityTypesPath', cacheserver+':/u/nlp/data/semparse/scr/freebase/freebase-rdf-2013-06-09-00-00.canonicalized.en-types'),
nil)
end
},
nil)
end
# tag is either "free917" or "webquestions"
def emnlp2013AblationExperiments(tag)
l(
letDefault(:ablation, 0),
# Ablation experiments (EMNLP)
sel(:ablation,
l(), # (0) Just run things normally
selo(nil, 'Parser.beamSize', 200, 50, 10), # (1) Vary beam size
selo(nil, 'Dataset.trainFrac', 0.1, 0.2, 0.4, 0.6), # (2) Vary training set size
sel(nil, # (3) Structural: only do join or only do bridge
o('Grammar.tags', l(tag, 'join')),
o('Grammar.tags', l(tag, 'bridge')),
o('Grammar.tags', l(tag, 'inject')),
nil),
sel(nil, # (4) Features
o('FeatureExtractor.featureDomains', *(freebaseFeatureDomains+['lexAlign'])), # +lexAlign
o('FeatureExtractor.featureDomains', *(freebaseFeatureDomains+['lexAlign']-['alignmentScores'])), # +lexAlign -alignmentScores
o('FeatureExtractor.featureDomains', *(freebaseFeatureDomains-['denotation'])), # -denotation
o('FeatureExtractor.featureDomains', *(freebaseFeatureDomains-['skipPos', 'joinPos'])), # -syntax features (skipPos, joinPos)
nil),
#o('Builder.executor', 'FormulaMatchExecutor'), # (6) train on logical forms (doesn't really work well)
nil),
letDefault(:split, 0), selo(:split, 'Dataset.splitRandom', 1, 2, 3),
nil)
end
def free917
l( # Data
letDefault(:data, 0),
sel(:data,
l(o('Dataset.inPaths', 'train,data/free917.train.examples.canonicalized.json'), unbalancedTrainDevSplit), # (0) train 0.8, dev 0.2
l(o('Dataset.inPaths', 'train,data/free917.train.examples.canonicalized.json', 'test,data/free917.test.examples.canonicalized.json')), # (1) Don't run on test yet!
nil),
# Grammar
o('Grammar.inPaths', 'freebase/data/emnlp2013.grammar'),
o('Parser.beamSize', 500),
emnlp2013AblationExperiments('free917'),
# lexicon index
letDefault(:lucene, 0),
sel(:lucene,
l(
o('EntityLexicon.exactMatchIndex','lib/lucene/4.4/free917/'),
cachePaths('10/LexiconFn.cache', '10/SparqlExecutor.cache'),
o('Grammar.tags', 'free917', 'bridge', 'join', 'inject', 'exact'),
nil),
l( # With entity disambiguation - currently too crappy
o('EntityLexicon.inexactMatchIndex','lib/lucene/4.4/inexact/'),
cachePaths('4/LexiconFn.cache', '4/SparqlExecutor.cache'),
o('Grammar.tags', 'free917', 'bridge', 'join', 'inject', 'inexact'),
nil),
nil),
# Use binary predicate features (overfits on free917)
o('BridgeFn.filterBadDomain',false),
# Learning
o('Learner.maxTrainIters', 6),
nil)
end
def webquestions
l(
# Data
letDefault(:data, 0),
sel(:data,
l( # Webquestions (dev) [EMNLP final JSON]
o('Dataset.inPaths',
'train,lib/data/webquestions/dataset_11/webquestions.examples.train.json'),
unbalancedTrainDevSplit,
nil),
l( # Webquestions (test) [EMNLP final JSON]
o('Dataset.inPaths',
'train,lib/data/webquestions/dataset_11/webquestions.examples.train.json',
'test,lib/data/webquestions/dataset_11/webquestions.examples.test.json'),
nil),
nil),
# Grammar
letDefault(:grammar, 1),
sel(:grammar, l(), l(o('Grammar.inPaths', 'freebase/data/emnlp2013.grammar'))),
o('Parser.beamSize', 200), # {07/03/13}: WebQuestions is too slow to run with default 500, so set to 200 for now...
# Caching
letDefault(:entitysearch, 0),
sel(:entitysearch, # Used for EMNLP 2013
l(
cachePaths('lucene/0.cache', 'sparql/3.cache'),
o('EntityLexicon.inexactMatchIndex','lib/lucene/4.4/inexact/'),
o('LexiconFn.maxEntityEntries',10),
o('Grammar.tags', 'webquestions', 'bridge', 'join', 'inject','inexact'), # specify also strategy
nil),
nil),
# Learning
o('Learner.maxTrainIters', 3),
# Use binary predicate features (overfits on free917)
o('BridgeFn.useBinaryPredicateFeatures', true),
o('BridgeFn.filterBadDomain',true),
letDefault(:split, 0), selo(:split, 'Dataset.splitRandom', 1,2,3),
nil)
end
addMode('freebase', 'Freebase (for EMNLP 2013, ACL 2014, TACL 2014)', lambda { |e| l(
letDefault(:train, 0),
letDefault(:interact, 0),
# nlpsub: for running commands on PBS
letDefault(:nlpsub, 0),
sel(:nlpsub,
l(),
l('nlpsub', '-d/scr/yonatan/sandbox/blackhole', '-nyonatan', '-c3'),
l('nlpsub', '-d/scr/yonatan/sandbox/blackhole', '-nyonatan', '-qjag', '-c3'),
l('nlpsub', '-d/scr/yonatan/sandbox/blackhole', '-nyonatan', '-qjohn', '-c3'),
nil),
sel(:interact, l()),
freebaseHeader,
'edu.stanford.nlp.sempre.Main',
freebaseOpts,
# Dataset
sel(:domain, {
'webquestions' => webquestions,
'free917' => free917,
}),
sel(:interact, l(), l(
# After training, run interact, which loads up a set of parameters and
# puts you in a prompt.
o('Dataset.inPaths'),
o('Learner.maxTrainIters', 0),
required(:load, 'none or exec number (e.g., 15) to load'),
lambda { |e|
if e[:load] == 'none' then
l()
else
execPath = "lib/models/#{e[:load]}.exec"
l(
o('Builder.inParamsPath', execPath+'/params'),
o('Grammar.inPaths', execPath+'/grammar'),
o('Master.logPath', lambda{|e| 'state/' + e[:domain] + '.log'}),
o('Master.newExamplesPath', lambda{|e| 'state/' + e[:domain] + '.examples'}),
o('Master.onlineLearnExamples', true),
# Make sure features are set properly!
nil)
end
},
o('Main.interactive'),
nil))
) })
addMode('cacheserver', 'Start the general-purpose cache server that serves files with key-value maps', lambda { |e|
l(
'java', '-Xmx36g', '-ea', '-cp', 'libsempre/*:lib/fig.jar',
'edu.stanford.nlp.sempre.cache.StringCacheServer',
letDefault(:port, 4000),
lambda { |e| o('port', e[:port]) },
letDefault(:cachetype, 0),
sel(:cachetype,
l(
o('FileStringCache.appendMode'),
o('FileStringCache.capacity', 35 * 1024),
o('FileStringCache.flushFrequency', 2147483647),
nil),
l(
o('FileStringCache.appendMode',false),
o('FileStringCache.capacity', 1 * 1024),
o('FileStringCache.flushFrequency', 100000),
nil),
nil),
nil)
})
############################################################
# Freebase RDF database (for building SPARQL database)
# Scratch directory
def scrOptions
letDefault(:scr, '/u/nlp/data/semparse/rdf/scr/' + `hostname | cut -f 1 -d .`.chomp)
end
addMode('filterfreebase', '(1) Filter RDF Freebase dump (do this once) [takes about 1 hour]', lambda { |e| l(
scrOptions,
l(
'fig/bin/qcreate', o('statePath', lambda{|e| e[:scr] + '/state'}),
'java', '-ea', '-Xmx20g', '-cp', 'libsempre/*:lib/*',
'edu.stanford.nlp.sempre.freebase.FilterFreebase',
o('inPath', '/u/nlp/data/semparse/scr/freebase/freebase-rdf-2013-06-09-00-00.canonicalized'),
sel(:keep, {
'all' => o('keepAllProperties'),
'geo' => l(
o('keepTypesPaths', 'data/geo.types'),
o('keepPropertiesPath', 'data/geo.properties'),
o('keepGeneralPropertiesOnlyForSeenEntities', true),
nil),
}),
o('execDir', '_OUTPATH_'), o('overwriteExecDir'),
nil),
nil) })
addMode('sparqlserver', '(2) Start the SPARQL server [do this every time]', lambda { |e| l(
scrOptions,
required(:exec),
sel(nil,
l(
'freebase/scripts/virtuoso', 'start',
lambda{|e| e[:scr]+'/state/execs/'+e[:exec].to_s+'.exec/vdb'}, # DB directory
lambda{|e| 3000+e[:exec]}, # port
nil),
# Give everyone permissions so that anyone can kill the server if needed.
l(
'chmod', '-R', 'og=u',
lambda{|e| e[:scr]+'/state/execs/'+e[:exec].to_s+'.exec/vdb'}, # DB directory
nil),
# To stop the server: freebase/scripts/virtuoso stop 3093
nil),
nil) })
# (3) Index the filtered RDF dump [takes 48 hours]
addMode('indexfreebase', '(3) Index the filtered RDF dump [takes 48 hours for Freebase]', lambda { |e| l(
letDefault(:stage, nil),
scrOptions,
required(:exec),
sel(:stage,
l(
'scripts/virtuoso', 'add',
lambda{|e| e[:scr]+'/state/execs/'+e[:exec].to_s+'.exec/0.ttl'}, # ttl file
lambda{|e| 3000+e[:exec]}, # port
lambda{|e| e[:offset] || 0}, # offset
nil),
l(
'scripts/extract-freebase-schema.rb',
lambda{|e| 'http://localhost:'+(3000+e[:exec]).to_s+'/sparql'}, # port
lambda{|e| e[:scr]+'/state/execs/'+e[:exec].to_s+'.exec/schema.ttl'},
nil),
nil),
nil) })
addMode('convertfree917', 'Convert the Free917 dataset', lambda { |e| l(
'java', '-ea', '-Xmx15g',
'-cp', 'libsempre/*:lib/*',
'edu.stanford.nlp.sempre.freebase.Free917Converter',
o('inDir','/u/nlp/data/semparse/yates/final-dataset-acl-2013-all/'),
o('outDir','data/free917_convert/'),
o('entityInfoFile','/user/joberant/scr/fb_data/3/entityInfo.txt'),
o('cvtFile','lib/fb_data/2/Cvts.txt'),
o('midToIdFile','/u/nlp/data/semparse/scr/freebase/freebase-rdf-2013-06-09-00-00.canonical-id-map'),
nil) })
addMode('query', 'Query a single logical form or SPARQL', lambda { |e| l(
codalab,
'java', '-ea',
'-cp', 'libsempre/*:lib/*',
'edu.stanford.nlp.sempre.freebase.SparqlExecutor',
sparqlOpts,
nil) })
############################################################
# Just start a simple interactive shell to try out SEMPRE commands
addMode('simple', 'Simple shell', lambda { |e| l(
codalab, 'java', '-cp', 'libsempre/*:lib/*', '-ea', 'edu.stanford.nlp.sempre.Main',
o('Main.interactive'),
nil) })
addMode('simple-sparql', 'Simple shell for querying SPARQL', lambda { |e| l(
codalab, 'java', '-Dmodules=core,freebase', '-cp', 'libsempre/*:lib/*', '-ea', 'edu.stanford.nlp.sempre.Main',
o('executor', 'freebase.SparqlExecutor'),
sparqlOpts,
o('Main.interactive'),
nil) })
addMode('simple-lambdadcs', 'Simple shell for querying with the LambdaDCSExecutor', lambda { |e| l(
codalab, 'java', '-Dmodules=core,tables,corenlp', '-cp', 'libsempre/*:lib/*', '-ea', 'edu.stanford.nlp.sempre.Main',
o('executor', 'tables.lambdadcs.LambdaDCSExecutor'),
o('FeatureExtractor.featureDomains', 'denotation lexAlign joinPos skipPos'.split),
o('LanguageAnalyzer.languageAnalyzer', 'corenlp.CoreNLPAnalyzer'),
o('Main.interactive'),
nil) })
addMode('simple-freebase', 'Simple shell for using Freebase', lambda { |e| l(
'java', '-Dmodules=core,freebase', '-cp', 'libsempre/*:lib/*', '-ea', 'edu.stanford.nlp.sempre.Main',
o('executor', 'freebase.SparqlExecutor'),
letDefault(:sparqlserver, 'freebase.cloudapp.net:3093'),
letDefault(:cacheserver, 'freebase.cloudapp.net:4000'),
sparqlOpts,
# Set up Freebase search for entities
# Assume run following on the server (read-only and capacity are important!)
# ./run @mode=cacheserver -readOnly -capacity MAX -basePath lib/fb_data
o('FreebaseSearch.cachePath', 'FreebaseSearch.cache'),
o('EntityLexicon.mid2idPath', lambda { |e| e[:cacheserver] + ':freebase-rdf-2013-06-09-00-00.canonical-id-map.gz' }),
o('TypeInference.typeLookup', 'freebase.FreebaseTypeLookup'),
o('FreebaseTypeLookup.entityTypesPath', lambda { |e| e[:cacheserver] + ':freebase-rdf-2013-06-09-00-00.canonicalized.en-types.gz' }),
o('EntityLexicon.maxEntries', 2),
o('FeatureExtractor.featureDomains', 'rule'),
o('Parser.coarsePrune'),
o('JoinFn.typeInference'),
o('UnaryLexicon.unaryLexiconFilePath', '/dev/null'),
o('BinaryLexicon.binaryLexiconFilesPath', '/dev/null'),
#o('JoinFn.showTypeCheckFailures'), # Use this to debug
o('Grammar.inPaths', 'freebase/data/demo1.grammar'), # Override with your own custom grammar
o('SparqlExecutor.returnTable'),
#o('SparqlExecutor.includeSupportingInfo'), # Show full information
o('Main.interactive'),
nil) })
addMode('simple-freebase-nocache', 'Simple shell for using Freebase (without a cache server)', lambda { |e| l(
'java', '-Dmodules=core,freebase', '-cp', 'libsempre/*:lib/*', '-ea', 'edu.stanford.nlp.sempre.Main',
o('executor', 'freebase.SparqlExecutor'),
letDefault(:sparqlserver, 'freebase.cloudapp.net:3093'),
sparqlOpts,
o('FeatureExtractor.featureDomains', 'rule'),
o('Parser.coarsePrune'),
o('JoinFn.typeInference'),
o('UnaryLexicon.unaryLexiconFilePath', '/dev/null'),
o('BinaryLexicon.binaryLexiconFilesPath', '/dev/null'),
#o('JoinFn.showTypeCheckFailures'), # Use this to debug
o('Grammar.inPaths', 'freebase/data/demo1.grammar'), # Override with your own custom grammar
#o('SparqlExecutor.includeSupportingInfo'), # Show full information
o('Main.interactive'),
nil) })
############################################################
# {2014-12-27} [Percy]: Overnight semantic parsing
def overnightFeatureDomains
[
'match',
'ppdb',
'skip-bigram',
'root',
'alignment',
'lexical',
'root_lexical',
'lf',
'simpleworld',
nil].compact
end
addMode('overnight', 'Overnight semantic parsing', l(
header('core,freebase,overnight'),
'edu.stanford.nlp.sempre.Main',
figOpts,
o('JavaExecutor.convertNumberValues', false),
o('useAnchorsOnce', true),
o('trackLocalChoices'),
o('JoinFn.typeInference', true),
o('Builder.parser', 'FloatingParser'),
o('FloatingParser.executeAllDerivations', 'true'),
o('LanguageAnalyzer', 'corenlp.CoreNLPAnalyzer'),
o('Learner.maxTrainIters', 1),
#o('printAllPredictions'),
o('Derivation.showUtterance'),
letDefault(:debug, 0),
selo(1, 'maxExamples', 'train:10', 'train:MAX'),
# Exact matching is needed on most simple domains
# o('executor', 'FormulaMatchExecutor'),
# o('Builder.valueEvaluator', 'ExactValueEvaluator'),
# Features
o('FeatureExtractor.featureDomains', 'denotation'), # denotation features from general feature extractor
o('FeatureExtractor.featureComputers', 'overnight.OvernightFeatureComputer'), #
o('OvernightFeatureComputer.featureDomains', *overnightFeatureDomains),
#o('initialization', 'paraphrase :: match,1', 'paraphrase :: size,-0.1', 'paraphrase :: ppdb,0.3',
# 'paraphrase :: skip-bigram,0.8', 'paraphrase :: skip-ppdb,0.2','denotation :: error,-1000'),
o('coarsePrune'),
sel(2,
l(), # no reg
l(o('Params.l1Reg','lazy'),o('Params.l1RegCoeff',0)),
l(o('Params.l1Reg','lazy'),o('Params.l1RegCoeff',0.001)),
nil),
# Set up the domain
required(:domain),
o('Grammar.inPaths', lambda { |e| 'overnight/' + e[:domain] + '.grammar' }),
o('SimpleWorld.domain', lambda { |e| e[:domain] }),
o('PPDBModel.ppdbModelPath', lambda { |e| 'lib/data/overnight/' + e[:domain] + '-ppdb.txt' }),
o('Dataset.trainFrac', 0.8), o('Dataset.devFrac', 0.2),
o('FloatingParser.maxDepth', 11),
o('Parser.beamSize', 20),
letDefault(:alignment, 1),
sel(:alignment,
o('wordAlignmentPath', lambda { |e| 'lib/data/overnight/' + e[:domain] + '.word_alignments.heuristic' }),
o('wordAlignmentPath', lambda { |e| 'lib/data/overnight/' + e[:domain] + '.word_alignments.berkeley' }),
nil),
o('phraseAlignmentPath', lambda { |e| 'lib/data/overnight/' + e[:domain] + '.phrase_alignments' }),
o('PPDBModel.ppdbModelPath', lambda { |e| 'lib/data/overnight/' + e[:domain] + '-ppdb.txt' }),
o('DerivationPruner.pruningComputers', ['overnight.OvernightDerivationPruningComputer']),
o('DerivationPruner.pruningStrategies', ['violateHardConstraints']),
o('Dataset.inPaths',
lambda { |e| 'train:lib/data/overnight/' + e[:domain] + '.paraphrases.train.examples' },
lambda { |e| 'test:lib/data/overnight/' + e[:domain] + '.paraphrases.test.examples' }),
sel(:domain, {
'geo880' => l(
letDefault(:data,0),
sel(:data,
l(o('Dataset.inPaths', 'train:lib/data/overnight/geo880.paraphrases.train.superlatives.examples')),
l(o('Dataset.inPaths', 'train:lib/data/overnight/geo880.paraphrases.train.superlatives.examples', 'test:lib/data/overnight/geo880-train.examples')),
l(o('Dataset.inPaths', 'train:lib/data/overnight/geo880.paraphrases.train.superlatives2.examples', 'test:lib/data/overnight/geo880-train.examples')),
l(o('Dataset.inPaths', 'train:lib/data/overnight/geo880.paraphrases.train.superlatives.examples', 'test:lib/data/overnight/geo880-test.examples')),
l(o('Dataset.inPaths', 'train:lib/data/overnight/geo880.paraphrases.train.superlatives2.examples', 'test:lib/data/overnight/geo880-test.examples')),
nil),
o('Parser.beamSize', 20),
o('initialization', 'paraphrase :: match,1', 'paraphrase :: size,-0.1', 'paraphrase :: ppdb,0.3',
'lf :: edu.stanford.nlp.sempre.SimpleWorld.superlative& superlative,10',
'root :: pos0=WRB&returnType=class edu.stanford.nlp.sempre.NumberValue,10'),
o('FloatingParser.maxDepth', 11),
o('Grammar.tags','generate','general', 'geo880'),
o('SimpleLexicon.inPaths', 'lib/data/overnight/geo880.lexicon'),
nil),
'calendar' => l(
o('Grammar.tags','generate','general'),
nil),
'calendarplus' => l(
o('Grammar.tags','generate','general','geo440'),
o('Grammar.inPaths','overnight/calendar.grammar'),
o('SimpleWorld.domain', 'calendar'),
nil),
'blocks' => l(
o('Grammar.tags','generate','general'),
nil),
'restaurants' => l(
o('Grammar.tags','generate','general'),
nil),
'housing' => l(
o('Grammar.tags','generate','general'),
nil),
'socialnetwork' => l(
o('Grammar.tags','generate','general'),
nil),
'publications' => l(
o('Grammar.tags','generate','general'),
nil),
'basketball' => l(
o('Grammar.tags','generate','general'),
nil),
'recipes' => l(
o('Grammar.tags','generate','general'),
nil),
}),
nil))
############################################################
# {5/27/15} [Ice]
addMode('tables', 'QA on HTML tables', lambda { |e| l(
# Add @cldir=1 to use CodaLab's directory paths
letDefault(:cldir, 0),
# Usual header
header('core,tables,corenlp,cprune'),
# Select class
letDefault(:class, 'main'),
sel(:class, {
'main' => 'edu.stanford.nlp.sempre.Main',
'check' => 'edu.stanford.nlp.sempre.tables.test.DPDParserChecker',
'dump' => 'edu.stanford.nlp.sempre.tables.serialize.SerializedDumper',
'load' => l('edu.stanford.nlp.sempre.tables.serialize.SerializedLoader', let(:parser, 'serialized')),
'stats' => 'edu.stanford.nlp.sempre.tables.test.TableStatsComputer',
'tag-data' => 'edu.stanford.nlp.sempre.tables.serialize.TaggedDatasetGenerator',
'tag-table' => 'edu.stanford.nlp.sempre.tables.serialize.TaggedTableGenerator',
'tag-fuzzy' => 'edu.stanford.nlp.sempre.tables.serialize.TaggedFuzzyGenerator',
'alter' => l('edu.stanford.nlp.sempre.tables.alter.BatchTableAlterer', let(:parser, 'serialized')),
'alter-ex' => l('edu.stanford.nlp.sempre.tables.alter.AlteredTablesExecutor', let(:parser, 'serialized')),
'filter' => 'edu.stanford.nlp.sempre.tables.serialize.DumpFilterer',
'column' => 'edu.stanford.nlp.sempre.tables.test.TableColumnAnalyzer',
'execute' => 'edu.stanford.nlp.sempre.tables.test.BatchTableExecutor',
}),
# Fig parameters
selo(:cldir, 'execDir', '_OUTPATH_', '.'),
o('overwriteExecDir'), o('addToView', 15), o('jarFiles', 'libsempre/*'),
sel(:cldir, l(), '>/dev/null'),
# Set environment for table execution
o('executor', 'tables.lambdadcs.LambdaDCSExecutor'),
o('targetValuePreprocessor', 'tables.TableValuePreprocessor'),
o('NumberFn.unitless'), o('NumberFn.alsoTestByConversion'),
o('TypeInference.typeLookup', 'tables.TableTypeLookup'),
o('JoinFn.specializedTypeCheck', false), o('JoinFn.typeInference', true),
o('Learner.outputPredValues'),
# Value Evaluator
letDefault(:eval, 'value'),
sel(:eval, {
'value' => o('Builder.valueEvaluator', 'tables.TableValueEvaluator'),
'denotation' => o('Builder.valueEvaluator', 'tables.TableValueEvaluator'), # alias of 'value'
'formula' => l(
o('Builder.valueEvaluator', 'tables.TableFormulaEvaluator'),
o('fallBackToValueEvaluator', false),
nil),
}),
# Parser
letDefault(:parser, 'floatsize'),
o('beamSize', 50),
o('useSizeInsteadOfDepth'),
sel(:parser, {
'floatsize' => l(
o('Builder.parser', 'FloatingParser'),
o('FloatingParser.maxDepth', 15),
nil),
'baseline' => o('Builder.parser', 'tables.baseline.TableBaselineParser'),
'serialized' => o('Builder.parser', 'tables.serialize.SerializedParser'),
# ACL 2016
'grow-dpd' => l(
o('Builder.parser', 'tables.dpd.DPDParser'),
o('FloatingParser.maxDepth', 8),
nil),
'grow-float' => l(
o('Builder.parser', 'FloatingParser'),
o('FloatingParser.maxDepth', 8),
o('FloatingParser.betaReduce'), o('initialFloatingHasZeroDepth'),
nil),
'grow-mix' => l(
o('Builder.parser', 'MixParser'),
o('MixParser.parsers', 'FloatingParser', 'tables.serialize.SerializedParser:train-0xc'),
o('FloatingParser.maxDepth', 8),
o('FloatingParser.betaReduce'), o('initialFloatingHasZeroDepth'),
nil),
# EMNLP 2017
'cprune' => l(
o('Builder.parser', 'cprune.CPruneFloatingParser'),
o('FloatingParser.maxDepth', 15),
o('maxNumNeighbors', 40),
o('maxPredictedPatterns', 1000),
nil),
}),
o('Parser.verbose', 0),
letDefault(:pruning, 1),
sel(:pruning,
l(),
l(
o('DerivationPruner.pruningStrategies', *tablesPruningStrategies),
o('DerivationPruner.pruningComputers', 'tables.TableDerivationPruningComputer'),
nil),
nil),
# Grammar
tablesGrammarPaths,
letDefault(:fuzzy, 'original'),
sel(:fuzzy, {
'original' => o('FuzzyMatcher.fuzzyMatcher', 'tables.match.OriginalMatcher'),
'editdist-exact' => l(
o('FuzzyMatcher.fuzzyMatcher', 'tables.match.EditDistanceFuzzyMatcher'),
o('fuzzyMatchMaxEditDistanceRatio', 0.0),
nil),
'editdist-fuzzy' => l(
o('FuzzyMatcher.fuzzyMatcher', 'tables.match.EditDistanceFuzzyMatcher'),
o('fuzzyMatchSubstring'), o('fuzzyMatchMaxEditDistanceRatio', 0.15),
o('alsoMatchPart'),
nil),
}),
letDefault(:normalize, 1),
sel(:normalize,
l(),
l(o('genericDateValue'), o('numberCanStartAnywhere'), o('num2CanStartAnywhere')),
nil),
letDefault(:anchor, 1),
sel(:anchor, {
1 => o('FloatingParser.useAnchorsOnce', true),
2 => l(o('FloatingParser.useAnchorsOnce', false), o('FloatingParser.useMaxAnchors', 2)),
}),
# Dataset
letDefault(:data, 'none'),
tablesDataPaths,
# Verbosity
o('FeatureVector.ignoreZeroWeight'),
o('logFeaturesLimit', 10),
o('LambdaDCSException.noErrorMessage'),
letDefault(:verbose, 0),
sel(:verbose,
l(
o('maxPrintedPredictions', 1), o('maxPrintedTrue', 1),
nil),
l(
o('maxPrintedPredictions', 10), o('maxPrintedTrue', 10),
o('putCellNameInCanonicalUtterance'), o('showUtterance'),
nil),
l(
o('maxPrintedPredictions', 10), o('maxPrintedTrue', 10),
o('putCellNameInCanonicalUtterance'), o('showUtterance'),
o('summarizeRuleTime'), o('summarizeDenotations'),
nil),
l(
o('maxPrintedPredictions', 10), o('maxPrintedTrue', 10),
o('putCellNameInCanonicalUtterance'), o('showUtterance'),
o('summarizeRuleTime'), o('summarizeDenotations'),
o('showRules'),
o('Parser.verbose', 2),
o('JoinFn.verbose', 3),
o('JoinFn.showTypeCheckFailures'),
nil),
nil),
# Language Analyzer
letDefault(:lang, 'corenlp'),
sel(:lang, {
'simple' => o('LanguageAnalyzer', 'SimpleAnalyzer'),
'corenlp' => l(o('LanguageAnalyzer', 'corenlp.CoreNLPAnalyzer'), o('annotators', *'tokenize ssplit pos lemma ner'.split)),
'fullcorenlp' => l(o('LanguageAnalyzer', 'corenlp.CoreNLPAnalyzer'), o('annotators', *'tokenize ssplit pos lemma ner parse'.split)),
}),
# Training
letDefault(:train, 0),
sel(:train,
l(
let(:l1, 0),
nil),
l(
o('combineFromFloatingParser'),
o('maxTrainIters', 3),
o('showValues', false), o('showFirstValue'),
o('customExpectedCounts', 'TOP'),
nil),
l(
# for dumping derivations (@class=dump)
# force unbalancedTrainDevSplit + combine from floating parser
o('combineFromFloatingParser'), o('DPDParser.cheat'),
nil),
nil),
# Regularization
letDefault(:l1, 1),
sel(:l1,
l(),
l(o('Params.l1Reg','lazy'), o('Params.l1RegCoeff', '3e-5')), # Default
l(o('Params.l1Reg','lazy'), selo(nil, 'Params.l1RegCoeff', 0, 0.00001, 0.0001, 0.001, 0.01)),
l(o('Params.l1Reg','lazy'), selo(nil, 'Params.l1RegCoeff', 0.00001, 0.00003, 0.0001, 0.0003)),
l(o('Params.l1Reg','lazy'), selo(nil, 'Params.l1RegCoeff', 0.00001, 0.00003, 0.0005)),
nil),
# Features
letDefault(:feat, 'none'),
sel(:feat, {
'none' => l(), # No features (random)
'some' => l( # Add your own features! (only set up the feature computers)
o('FeatureExtractor.featureComputers', 'tables.features.PhrasePredicateFeatureComputer tables.features.PhraseDenotationFeatureComputer'.split),
nil),
'all' => l( # All ACL 2015 features
o('FeatureExtractor.featureDomains', 'custom-denotation phrase-predicate phrase-denotation headword-denotation missing-predicate'.split),
o('FeatureExtractor.featureComputers', 'tables.features.PhrasePredicateFeatureComputer tables.features.PhraseDenotationFeatureComputer'.split),
nil),
'more' => l( # All ACL 2015 features + more experimental features
o('FeatureExtractor.featureDomains', 'custom-denotation phrase-predicate phrase-denotation headword-denotation missing-predicate anchored-entity'.split),
o('FeatureExtractor.featureComputers', 'tables.features.PhrasePredicateFeatureComputer tables.features.PhraseDenotationFeatureComputer tables.features.AnchorFeatureComputer'.split),
nil),
'baseline' => l( # For the baseline classifier
o('FeatureExtractor.featureDomains', 'custom-denotation phrase-denotation headword-denotation table-baseline'.split),
o('FeatureExtractor.featureComputers', 'tables.baseline.TableBaselineFeatureComputer tables.features.PhraseDenotationFeatureComputer'.split),
nil),
'ablate' => l(
o('FeatureExtractor.featureComputers', 'tables.features.PhrasePredicateFeatureComputer tables.features.PhraseDenotationFeatureComputer'.split),
selo(nil,
'FeatureExtractor.featureDomains',
'phrase-predicate phrase-denotation headword-denotation missing-predicate'.split,
'custom-denotation phrase-denotation headword-denotation missing-predicate'.split,
'custom-denotation phrase-predicate headword-denotation missing-predicate'.split,
'custom-denotation phrase-predicate phrase-denotation missing-predicate'.split,
'custom-denotation phrase-predicate phrase-denotation headword-denotation'.split,
nil),
nil),
}),
letDefault(:featOp, 'careful'),
sel(:featOp, {
'none' => l(),
'careful' => l(
o('maxNforLexicalizeAllPairs', 2),
o('computeFuzzyMatchPredicates'),
nil),
}),
nil) })
def tablesGrammarPaths
lambda { |e|
baseDir = ['tables/grammars/', 'grammars/'][e[:cldir]]
l(
letDefault(:grammar, 'combined-all'),
sel(:grammar, {
'custom' => l(),
'restrict' => o('Grammar.inPaths', "#{baseDir}restrict.grammar"),
'simple' => o('Grammar.inPaths', "#{baseDir}simple.grammar"),
'combined' => o('Grammar.inPaths', "#{baseDir}combined.grammar"),
'combined-jnc' => l( # WQ baseline
o('Grammar.inPaths', "#{baseDir}combined.grammar"),
o('Grammar.tags', *'movement count'.split),
nil),
'combined-cut' => l( # No intersection / union
o('Grammar.inPaths', "#{baseDir}combined.grammar"),
o('Grammar.tags', *'movement comparison count aggregate superlative arithmetic'.split),
nil),
'combined-all' => l( # Default
o('Grammar.inPaths', "#{baseDir}combined.grammar"),
o('Grammar.tags', *'alternative movement comparison count aggregate superlative arithmetic merge'.split),
nil),
'combined-more' => l(
o('Grammar.inPaths', "#{baseDir}combined.grammar"),
o('Grammar.tags', *'alternative movement comparison count aggregate superlative arithmetic merge v-superlative'.split),
nil),
'combined-trigger' => l( # Use trigger words for operations
o('Grammar.inPaths', "#{baseDir}combined.grammar"),
o('Grammar.tags', *'t-alternative t-movement t-comparison t-count t-aggregate t-superlative t-arithmetic merge'.split),
nil),
# ACL 2016
'grow-custom' => l(
o('Grammar.inPaths', "#{baseDir}grow.grammar"),
o('Grammar.binarizeRules', false),
nil),
'grow-default' => l(
o('Grammar.inPaths', "#{baseDir}grow.grammar"),
o('Grammar.binarizeRules', false),
o('Grammar.tags', *'scoped merge-and arithmetic comparison alternative neq yearrange part closedclass scoped-2args-merge-and'.split),
let(:anchor, 2),
nil),
'grow-strict' => l(
o('Grammar.inPaths', "#{baseDir}grow.grammar"),
o('Grammar.binarizeRules', false),
o('Grammar.tags', *'scoped merge-and arithmetic comparison alternative neq yearrange part closedclass-generic scoped-2args-merge-and'.split),
let(:anchor, 2),
nil),
# EMNLP 2017
'extended' => l(
o('Grammar.inPaths', "#{baseDir}extended.grammar"),
o('Grammar.tags', *'alternative movement comparison count aggregate superlative arithmetic merge v-superlative'.split),
nil),
}),
nil)
}
end
def tablesDataPaths
lambda { |e|
baseDir = ['lib/data/WikiTableQuestions/data/', 'WikiTableQuestions/data/'][e[:cldir]]
csvDir = ['lib/data/WikiTableQuestions/', 'WikiTableQuestions/'][e[:cldir]]
nnDir = ['lib/data/nn_0/', 'nn_0/'][e[:cldir]]
datasets = {
'none' => l(),
'train' => o('Dataset.inPaths', "train,#{baseDir}training.examples"),
# Pristine test test
'test' => l(
o('Dataset.inPaths',
"train,#{baseDir}training.examples",
"test,#{baseDir}pristine-unseen-tables.examples"),
o('neighborFilePath', "#{nnDir}/exact_nearest_neighbors.all"),
nil),
# @data=annotated can be used with @class=check only
'annotated' => o('Dataset.inPaths', "train,#{baseDir}annotated-all.examples"),
'before300' => o('Dataset.inPaths', "train,#{baseDir}training-before300.examples"),
}
# Development sets: 80:20 random split of training data
['1', '2', '3', '4', '5'].each do |x|
datasets['u-' + x] = l(
o('Dataset.inPaths',
"train,#{baseDir}random-split-#{x}-train.examples",
"dev,#{baseDir}random-split-#{x}-dev.examples",
nil),
o('neighborFilePath', "#{nnDir}/exact_nearest_neighbors.seed-#{x}.train"),
nil)
end
# That's it!
l(
o('TableKnowledgeGraph.baseCSVDir', csvDir),
# To use the normalized values from the tagged file, which were checked by hand,
# add @useTaggedFile=1
letDefault(:useTaggedFile, 0),
selo(:useTaggedFile, 'TableValuePreprocessor.taggedFiles', '', "#{csvDir}/tagged/data/"),
sel(:data, datasets),
nil)
}
end
def tablesPruningStrategies
[
### Critical strategies
"emptyDenotation",
"nonLambdaError",
### Strategies that do not depend on the children's actual formulas
"atomic",
"tooManyValues",
"badSummarizerHead",
"mistypedMerge",
### Strategies that depend on the children's formulas
"doubleNext",
"multipleSuperlatives",
"sameMerge",
"forwardBackward",
"unsortedMerge",
"typeRowMerge",
nil].compact
end
############################################################
# {2015-01-18} Generate utterances [Percy]
addMode('genovernight', 'Generate utterances for overnight semantic parsing', lambda { |e| l(
header('core,overnight'),
'edu.stanford.nlp.sempre.overnight.GenerationMain',
figOpts,
o('JoinFn.typeInference', true),
o('JoinFn.specializedTypeCheck', false),
o('JavaExecutor.convertNumberValues', false),
o('JavaExecutor.printStackTrace', false),
# These domains are all based on SimpleWorld
required(:domain),
o('Grammar.inPaths', lambda { |e| 'overnight/' + e[:domain] + '.grammar' }),
o('SimpleWorld.domain', lambda { |e| e[:domain] }),
o('initialization', 'denotation :: error,-1000', 'denotation :: empty,-100', 'paraphrase :: size,+0.01', 'denotation :: value_in_formula,-100'),
o('FeatureExtractor.featureComputers','overnight.OvernightFeatureComputer'),
o('OvernightFeatureComputer.featureDomains', ''),
o('OvernightFeatureComputer.itemAnalysis',false),
letDefault(:gen, 1),
sel(:gen,
l( # For debugging the grammar
o('FeatureExtractor.featureDomains', 'denotation'),
o('Dataset.inPaths', lambda { |e| 'train:overnight/' + e[:domain] + '-unittest.examples'}),
selo(:parse, 'Grammar.tags', 'generate', 'parse'),
o('interactive'),
nil),
l( # For generating utterances
o('parser', 'FloatingParser'),
o('maxDepth', 30), o('beamSize', 10000),
o('derivationScoreNoise', 1),
o('Dataset.inPaths', 'train:overnight/null.examples'),