diff options
| -rw-r--r-- | decoder/Makefile.am | 1 | ||||
| -rw-r--r-- | decoder/cdec_ff.cc | 3 | ||||
| -rw-r--r-- | decoder/ff_lexical.h | 128 | ||||
| -rw-r--r-- | decoder/ff_rules.cc | 22 | ||||
| -rw-r--r-- | decoder/ff_rules.h | 13 | ||||
| -rw-r--r-- | training/dtrain/examples/standard/cdec.ini | 2 | ||||
| -rw-r--r-- | training/dtrain/examples/standard/expected-output | 115 | ||||
| -rw-r--r-- | training/mira/kbest_cut_mira.cc | 8 | ||||
| -rwxr-xr-x | training/mira/mira.py | 4 |
9 files changed, 200 insertions, 96 deletions
diff --git a/decoder/Makefile.am b/decoder/Makefile.am index b735756d..c0371081 100644 --- a/decoder/Makefile.am +++ b/decoder/Makefile.am @@ -48,6 +48,7 @@ libcdec_a_SOURCES = \ ff_external.h \ ff_factory.h \ ff_klm.h \ + ff_lexical.h \ ff_lm.h \ ff_ngrams.h \ ff_parse_match.h \ diff --git a/decoder/cdec_ff.cc b/decoder/cdec_ff.cc index b2541722..8689a615 100644 --- a/decoder/cdec_ff.cc +++ b/decoder/cdec_ff.cc @@ -24,6 +24,7 @@ #include "ff_charset.h" #include "ff_wordset.h" #include "ff_external.h" +#include "ff_lexical.h" void register_feature_functions() { @@ -39,13 +40,13 @@ void register_feature_functions() { RegisterFF<SourceWordPenalty>(); RegisterFF<ArityPenalty>(); RegisterFF<BLEUModel>(); + RegisterFF<LexicalFeatures>(); //TODO: use for all features the new Register which requires static FF::usage(false,false) give name ff_registry.Register("SpanFeatures", new FFFactory<SpanFeatures>()); ff_registry.Register("NgramFeatures", new FFFactory<NgramDetector>()); ff_registry.Register("RuleContextFeatures", new FFFactory<RuleContextFeatures>()); ff_registry.Register("RuleIdentityFeatures", new FFFactory<RuleIdentityFeatures>()); - ff_registry.Register("RuleWordAlignmentFeatures", new FFFactory<RuleWordAlignmentFeatures>()); ff_registry.Register("ParseMatchFeatures", new FFFactory<ParseMatchFeatures>); ff_registry.Register("SoftSyntaxFeatures", new FFFactory<SoftSyntaxFeatures>); ff_registry.Register("SoftSyntaxFeaturesMindist", new FFFactory<SoftSyntaxFeaturesMindist>); diff --git a/decoder/ff_lexical.h b/decoder/ff_lexical.h new file mode 100644 index 00000000..21c85b27 --- /dev/null +++ b/decoder/ff_lexical.h @@ -0,0 +1,128 @@ +#ifndef FF_LEXICAL_H_ +#define FF_LEXICAL_H_ + +#include <vector> +#include <map> +#include "trule.h" +#include "ff.h" +#include "hg.h" +#include "array2d.h" +#include "wordid.h" +#include <sstream> +#include <cassert> +#include <cmath> + +#include "filelib.h" +#include "stringlib.h" +#include "sentence_metadata.h" +#include "lattice.h" +#include "fdict.h" +#include "verbose.h" +#include "tdict.h" +#include "hg.h" + +using namespace std; + +namespace { + string Escape(const string& x) { + string y = x; + for (int i = 0; i < y.size(); ++i) { + if (y[i] == '=') y[i]='_'; + if (y[i] == ';') y[i]='_'; + } + return y; + } +} + +class LexicalFeatures : public FeatureFunction { +public: + LexicalFeatures(const std::string& param) { + if (param.empty()) { + cerr << "LexicalFeatures: using T,D,I\n"; + T_ = true; I_ = true; D_ = true; + } else { + const vector<string> argv = SplitOnWhitespace(param); + assert(argv.size() == 3); + T_ = (bool) atoi(argv[0].c_str()); + I_ = (bool) atoi(argv[1].c_str()); + D_ = (bool) atoi(argv[2].c_str()); + cerr << "T=" << T_ << " I=" << I_ << " D=" << D_ << endl; + } + }; + static std::string usage(bool p,bool d) { + return usage_helper("LexicalFeatures","[0/1 0/1 0/1]","Sparse lexical word translation indicator features. If arguments are supplied, specify like this: translations insertions deletions",p,d); + } +protected: + virtual void TraversalFeaturesImpl(const SentenceMetadata& smeta, + const HG::Edge& edge, + const std::vector<const void*>& ant_contexts, + SparseVector<double>* features, + SparseVector<double>* estimated_features, + void* context) const; + virtual void PrepareForInput(const SentenceMetadata& smeta); +private: + mutable std::map<const TRule*, SparseVector<double> > rule2feats_; + bool T_; + bool I_; + bool D_; +}; + +void LexicalFeatures::PrepareForInput(const SentenceMetadata& smeta) { + rule2feats_.clear(); // std::map<const TRule*, SparseVector<double> > +} + +void LexicalFeatures::TraversalFeaturesImpl(const SentenceMetadata& smeta, + const HG::Edge& edge, + const std::vector<const void*>& ant_contexts, + SparseVector<double>* features, + SparseVector<double>* estimated_features, + void* context) const { + + map<const TRule*, SparseVector<double> >::iterator it = rule2feats_.find(edge.rule_.get()); + if (it == rule2feats_.end()) { + const TRule& rule = *edge.rule_; + it = rule2feats_.insert(make_pair(&rule, SparseVector<double>())).first; + SparseVector<double>& f = it->second; + std::vector<bool> sf(edge.rule_->FLength(),false); // stores if source tokens are visited by alignment points + std::vector<bool> se(edge.rule_->ELength(),false); // stores if target tokens are visited by alignment points + int fid = 0; + // translations + for (unsigned i=0;i<rule.a_.size();++i) { + const AlignmentPoint& ap = rule.a_[i]; + sf[ap.s_] = true; // mark index as seen + se[ap.t_] = true; // mark index as seen + ostringstream os; + os << "LT:" << Escape(TD::Convert(rule.f_[ap.s_])) << ":" << Escape(TD::Convert(rule.e_[ap.t_])); + fid = FD::Convert(os.str()); + if (fid <= 0) continue; + if (T_) + f.add_value(fid, 1.0); + } + // word deletions + for (unsigned i=0;i<sf.size();++i) { + if (!sf[i] && rule.f_[i] > 0) {// if not visited and is terminal + ostringstream os; + os << "LD:" << Escape(TD::Convert(rule.f_[i])); + fid = FD::Convert(os.str()); + if (fid <= 0) continue; + if (D_) + f.add_value(fid, 1.0); + } + } + // word insertions + for (unsigned i=0;i<se.size();++i) { + if (!se[i] && rule.e_[i] >= 1) {// if not visited and is terminal + ostringstream os; + os << "LI:" << Escape(TD::Convert(rule.e_[i])); + fid = FD::Convert(os.str()); + if (fid <= 0) continue; + if (I_) + f.add_value(fid, 1.0); + } + } + } + (*features) += it->second; +} + + +#endif diff --git a/decoder/ff_rules.cc b/decoder/ff_rules.cc index 7bccf084..9533caed 100644 --- a/decoder/ff_rules.cc +++ b/decoder/ff_rules.cc @@ -69,28 +69,6 @@ void RuleIdentityFeatures::TraversalFeaturesImpl(const SentenceMetadata& smeta, features->add_value(it->second, 1); } -RuleWordAlignmentFeatures::RuleWordAlignmentFeatures(const std::string& param) { -} - -void RuleWordAlignmentFeatures::PrepareForInput(const SentenceMetadata& smeta) { -} - -void RuleWordAlignmentFeatures::TraversalFeaturesImpl(const SentenceMetadata& smeta, - const Hypergraph::Edge& edge, - const vector<const void*>& ant_contexts, - SparseVector<double>* features, - SparseVector<double>* estimated_features, - void* context) const { - const TRule& rule = *edge.rule_; - ostringstream os; - vector<AlignmentPoint> als = rule.als(); - std::vector<AlignmentPoint>::const_iterator xx = als.begin(); - for (; xx != als.end(); ++xx) { - os << "WA:" << TD::Convert(rule.f_[xx->s_]) << ":" << TD::Convert(rule.e_[xx->t_]); - } - features->add_value(FD::Convert(Escape(os.str())), 1); -} - RuleSourceBigramFeatures::RuleSourceBigramFeatures(const std::string& param) { } diff --git a/decoder/ff_rules.h b/decoder/ff_rules.h index 324d7a39..f210dc65 100644 --- a/decoder/ff_rules.h +++ b/decoder/ff_rules.h @@ -24,19 +24,6 @@ class RuleIdentityFeatures : public FeatureFunction { mutable std::map<const TRule*, int> rule2_fid_; }; -class RuleWordAlignmentFeatures : public FeatureFunction { - public: - RuleWordAlignmentFeatures(const std::string& param); - protected: - virtual void TraversalFeaturesImpl(const SentenceMetadata& smeta, - const HG::Edge& edge, - const std::vector<const void*>& ant_contexts, - SparseVector<double>* features, - SparseVector<double>* estimated_features, - void* context) const; - virtual void PrepareForInput(const SentenceMetadata& smeta); -}; - class RuleSourceBigramFeatures : public FeatureFunction { public: RuleSourceBigramFeatures(const std::string& param); diff --git a/training/dtrain/examples/standard/cdec.ini b/training/dtrain/examples/standard/cdec.ini index 6cba9e1e..3330dd71 100644 --- a/training/dtrain/examples/standard/cdec.ini +++ b/training/dtrain/examples/standard/cdec.ini @@ -21,7 +21,7 @@ feature_function=RuleIdentityFeatures feature_function=RuleSourceBigramFeatures feature_function=RuleTargetBigramFeatures feature_function=RuleShape -feature_function=RuleWordAlignmentFeatures +feature_function=LexicalFeatures 1 1 1 #feature_function=SourceSpanSizeFeatures #feature_function=SourceWordPenalty #feature_function=SpanFeatures diff --git a/training/dtrain/examples/standard/expected-output b/training/dtrain/examples/standard/expected-output index fa831221..2460cfbb 100644 --- a/training/dtrain/examples/standard/expected-output +++ b/training/dtrain/examples/standard/expected-output @@ -4,7 +4,8 @@ Reading ./nc-wmt11.en.srilm.gz ----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100 **************************************************************************************************** Example feature: Shape_S00000_T00000 -Seeding random number sequence to 4138446869 +T=1 I=1 D=1 +Seeding random number sequence to 2327685089 dtrain Parameters: @@ -36,87 +37,87 @@ Iteration #1 of 3. . 10 Stopping after 10 input sentences. WEIGHTS - Glue = -80.3 - WordPenalty = -51.247 - LanguageModel = +282.46 - LanguageModel_OOV = -85.8 - PhraseModel_0 = -100.06 - PhraseModel_1 = -98.692 - PhraseModel_2 = -9.4958 - PhraseModel_3 = +18.535 - PhraseModel_4 = +62.35 - PhraseModel_5 = +7 - PhraseModel_6 = +31.4 - PassThrough = -126.5 + Glue = +6.9 + WordPenalty = -46.426 + LanguageModel = +535.12 + LanguageModel_OOV = -123.5 + PhraseModel_0 = -160.73 + PhraseModel_1 = -350.13 + PhraseModel_2 = -187.81 + PhraseModel_3 = +172.04 + PhraseModel_4 = +0.90108 + PhraseModel_5 = +21.6 + PhraseModel_6 = +67.2 + PassThrough = -149.7 --- - 1best avg score: 0.25631 (+0.25631) - 1best avg model score: -4843.6 (-4843.6) - avg # pairs: 744.4 + 1best avg score: 0.23327 (+0.23327) + 1best avg model score: -9084.9 (-9084.9) + avg # pairs: 780.7 avg # rank err: 0 (meaningless) avg # margin viol: 0 k-best loss imp: 100% - non0 feature count: 1274 + non0 feature count: 1389 avg list sz: 91.3 - avg f count: 143.72 -(time 0.4 min, 2.4 s/S) + avg f count: 146.2 +(time 0.37 min, 2.2 s/S) Iteration #2 of 3. . 10 WEIGHTS - Glue = -117.4 - WordPenalty = -99.584 - LanguageModel = +395.05 - LanguageModel_OOV = -136.8 - PhraseModel_0 = +40.614 - PhraseModel_1 = -123.29 - PhraseModel_2 = -152 - PhraseModel_3 = -161.13 - PhraseModel_4 = -76.379 - PhraseModel_5 = +39.1 - PhraseModel_6 = +137.7 - PassThrough = -162.1 + Glue = -43 + WordPenalty = -22.019 + LanguageModel = +591.53 + LanguageModel_OOV = -252.1 + PhraseModel_0 = -120.21 + PhraseModel_1 = -43.589 + PhraseModel_2 = +73.53 + PhraseModel_3 = +113.7 + PhraseModel_4 = -223.81 + PhraseModel_5 = +64 + PhraseModel_6 = +54.8 + PassThrough = -331.1 --- - 1best avg score: 0.26751 (+0.011198) - 1best avg model score: -10061 (-5216.9) - avg # pairs: 639.1 + 1best avg score: 0.29568 (+0.062413) + 1best avg model score: -15879 (-6794.1) + avg # pairs: 566.1 avg # rank err: 0 (meaningless) avg # margin viol: 0 k-best loss imp: 100% - non0 feature count: 1845 + non0 feature count: 1931 avg list sz: 91.3 - avg f count: 139.88 -(time 0.35 min, 2.1 s/S) + avg f count: 139.89 +(time 0.33 min, 2 s/S) Iteration #3 of 3. . 10 WEIGHTS - Glue = -101.1 - WordPenalty = -139.97 - LanguageModel = +327.98 - LanguageModel_OOV = -234.7 - PhraseModel_0 = -144.49 - PhraseModel_1 = -263.88 - PhraseModel_2 = -149.25 - PhraseModel_3 = -38.805 - PhraseModel_4 = +50.575 - PhraseModel_5 = -52.4 - PhraseModel_6 = +41.6 - PassThrough = -230.2 + Glue = -44.3 + WordPenalty = -131.85 + LanguageModel = +230.91 + LanguageModel_OOV = -285.4 + PhraseModel_0 = -194.27 + PhraseModel_1 = -294.83 + PhraseModel_2 = -92.043 + PhraseModel_3 = -140.24 + PhraseModel_4 = +85.613 + PhraseModel_5 = +238.1 + PhraseModel_6 = +158.7 + PassThrough = -359.6 --- - 1best avg score: 0.36222 (+0.094717) - 1best avg model score: -17416 (-7355.5) - avg # pairs: 661.2 + 1best avg score: 0.37375 (+0.078067) + 1best avg model score: -14519 (+1359.7) + avg # pairs: 545.4 avg # rank err: 0 (meaningless) avg # margin viol: 0 k-best loss imp: 100% - non0 feature count: 2163 + non0 feature count: 2218 avg list sz: 91.3 - avg f count: 132.53 -(time 0.33 min, 2 s/S) + avg f count: 137.77 +(time 0.35 min, 2.1 s/S) Writing weights file to '-' ... done --- -Best iteration: 3 [SCORE 'fixed_stupid_bleu'=0.36222]. -This took 1.0833 min. +Best iteration: 3 [SCORE 'fixed_stupid_bleu'=0.37375]. +This took 1.05 min. diff --git a/training/mira/kbest_cut_mira.cc b/training/mira/kbest_cut_mira.cc index e0b6eecb..9de57f5f 100644 --- a/training/mira/kbest_cut_mira.cc +++ b/training/mira/kbest_cut_mira.cc @@ -95,7 +95,8 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) { ("stream,t", "Stream mode (used for realtime)") ("weights_output,O",po::value<string>(),"Directory to write weights to") ("output_dir,D",po::value<string>(),"Directory to place output in") - ("decoder_config,c",po::value<string>(),"Decoder configuration file"); + ("decoder_config,c",po::value<string>(),"Decoder configuration file") + ("verbose,v",po::value<bool>()->zero_tokens(),"verbose stderr output"); po::options_description clo("Command line options"); clo.add_options() ("config", po::value<string>(), "Configuration file") @@ -629,6 +630,7 @@ int main(int argc, char** argv) { vector<string> corpus; + const bool VERBOSE = conf.count("verbose"); const string metric_name = conf["mt_metric"].as<string>(); optimizer = conf["optimizer"].as<int>(); fear_select = conf["fear"].as<int>(); @@ -792,7 +794,8 @@ int main(int argc, char** argv) { double margin = cur_bad.features.dot(dense_weights) - cur_good.features.dot(dense_weights); double mt_loss = (cur_good.mt_metric - cur_bad.mt_metric); const double loss = margin + mt_loss; - cerr << "LOSS: " << loss << " Margin:" << margin << " BLEUL:" << mt_loss << " " << cur_bad.features.dot(dense_weights) << " " << cur_good.features.dot(dense_weights) <<endl; + cerr << "LOSS: " << loss << " Margin:" << margin << " BLEUL:" << mt_loss << endl; + if (VERBOSE) cerr << cur_bad.features.dot(dense_weights) << " " << cur_good.features.dot(dense_weights) << endl; if (loss > 0.0 || !checkloss) { SparseVector<double> diff = cur_good.features; diff -= cur_bad.features; @@ -929,6 +932,7 @@ int main(int argc, char** argv) { lambdas += (cur_pair[1]->features) * step_size; lambdas -= (cur_pair[0]->features) * step_size; + if (VERBOSE) cerr << " Lambdas " << lambdas << endl; //reload weights based on update dense_weights.clear(); diff --git a/training/mira/mira.py b/training/mira/mira.py index c84a8cff..1861da1a 100755 --- a/training/mira/mira.py +++ b/training/mira/mira.py @@ -143,6 +143,8 @@ def main(): parser.add_argument('--pass-suffix', help='multipass decoding iteration. see documentation ' 'at www.cdec-decoder.org for more information') + parser.add_argument('-v', '--verbose', + help='more verbose mira optimizers') args = parser.parse_args() args.metric = args.metric.upper() @@ -352,6 +354,8 @@ def optimize(args, script_dir, dev_size): decoder_cmd += ' -a' if not args.no_pseudo: decoder_cmd += ' -e' + if args.verbose: + decoder_cmd += ' -v' #always use fork parallel_cmd = '{0} --use-fork -e {1} -j {2} --'.format( |