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-rw-r--r--decoder/Makefile.am1
-rw-r--r--decoder/cdec_ff.cc3
-rw-r--r--decoder/ff_lexical.h128
-rw-r--r--decoder/ff_rules.cc22
-rw-r--r--decoder/ff_rules.h13
-rw-r--r--training/dtrain/examples/standard/cdec.ini2
-rw-r--r--training/dtrain/examples/standard/expected-output115
-rw-r--r--training/mira/kbest_cut_mira.cc8
-rwxr-xr-xtraining/mira/mira.py4
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 e075bed3..62c770df 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 77f2f35f..0980ef2e 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(
generated by cgit v1.2.3 (git 2.39.1) at 2024-09-22 17:36:34 +0000