1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
|
#include <stdexcept>
#include "moses/Incremental.h"
#include "moses/ChartCell.h"
#include "moses/ChartParserCallback.h"
#include "moses/FeatureVector.h"
#include "moses/StaticData.h"
#include "moses/Util.h"
#include "moses/LM/Base.h"
#include "moses/OutputCollector.h"
#include "lm/model.hh"
#include "search/applied.hh"
#include "search/config.hh"
#include "search/context.hh"
#include "search/edge_generator.hh"
#include "search/rule.hh"
#include "search/vertex_generator.hh"
#include <boost/lexical_cast.hpp>
namespace Moses
{
namespace Incremental
{
namespace
{
// This is called by EdgeGenerator. Route hypotheses to separate vertices for
// each left hand side label, populating ChartCellLabelSet out.
template <class Best> class HypothesisCallback
{
private:
typedef search::VertexGenerator<Best> Gen;
public:
HypothesisCallback(search::ContextBase &context, Best &best, ChartCellLabelSet &out, boost::object_pool<search::Vertex> &vertex_pool)
: context_(context), best_(best), out_(out), vertex_pool_(vertex_pool) {}
void NewHypothesis(search::PartialEdge partial) {
// Get the LHS, look it up in the output ChartCellLabel, and upcast it.
// It's not part of the union because it would have been ugly to expose template types in ChartCellLabel.
ChartCellLabel::Stack &stack = out_.FindOrInsert(static_cast<const TargetPhrase *>(partial.GetNote().vp)->GetTargetLHS());
Gen *entry = static_cast<Gen*>(stack.incr_generator);
if (!entry) {
entry = generator_pool_.construct(boost::ref(context_), boost::ref(*vertex_pool_.construct()), boost::ref(best_));
stack.incr_generator = entry;
}
entry->NewHypothesis(partial);
}
void FinishedSearch() {
for (ChartCellLabelSet::iterator i(out_.mutable_begin()); i != out_.mutable_end(); ++i) {
if ((*i) == NULL) {
continue;
}
ChartCellLabel::Stack &stack = (*i)->MutableStack();
Gen *gen = static_cast<Gen*>(stack.incr_generator);
gen->FinishedSearch();
stack.incr = &gen->Generating();
}
}
private:
search::ContextBase &context_;
Best &best_;
ChartCellLabelSet &out_;
boost::object_pool<search::Vertex> &vertex_pool_;
boost::object_pool<Gen> generator_pool_;
};
// This is called by the moses parser to collect hypotheses. It converts to my
// edges (search::PartialEdge).
template <class Model> class Fill : public ChartParserCallback
{
public:
Fill(search::Context<Model> &context, const std::vector<lm::WordIndex> &vocab_mapping, search::Score oov_weight)
: context_(context), vocab_mapping_(vocab_mapping), oov_weight_(oov_weight) {}
void Add(const TargetPhraseCollection &targets, const StackVec &nts, const WordsRange &ignored);
void AddPhraseOOV(TargetPhrase &phrase, std::list<TargetPhraseCollection*> &waste_memory, const WordsRange &range);
float GetBestScore(const ChartCellLabel *chartCell) const;
bool Empty() const {
return edges_.Empty();
}
template <class Best> void Search(Best &best, ChartCellLabelSet &out, boost::object_pool<search::Vertex> &vertex_pool) {
HypothesisCallback<Best> callback(context_, best, out, vertex_pool);
edges_.Search(context_, callback);
}
// Root: everything into one vertex.
template <class Best> search::History RootSearch(Best &best) {
search::Vertex vertex;
search::RootVertexGenerator<Best> gen(vertex, best);
edges_.Search(context_, gen);
return vertex.BestChild();
}
void EvaluateWithSourceContext(const InputType &input, const InputPath &inputPath) {
// TODO for input lattice
}
private:
lm::WordIndex Convert(const Word &word) const;
search::Context<Model> &context_;
const std::vector<lm::WordIndex> &vocab_mapping_;
search::EdgeGenerator edges_;
const search::Score oov_weight_;
};
template <class Model> void Fill<Model>::Add(const TargetPhraseCollection &targets, const StackVec &nts, const WordsRange &range)
{
std::vector<search::PartialVertex> vertices;
vertices.reserve(nts.size());
float below_score = 0.0;
for (StackVec::const_iterator i(nts.begin()); i != nts.end(); ++i) {
vertices.push_back((*i)->GetStack().incr->RootAlternate());
below_score += (*i)->GetBestScore(this);
}
std::vector<lm::WordIndex> words;
for (TargetPhraseCollection::const_iterator p(targets.begin()); p != targets.end(); ++p) {
words.clear();
const TargetPhrase &phrase = **p;
const AlignmentInfo::NonTermIndexMap &align = phrase.GetAlignNonTerm().GetNonTermIndexMap();
search::PartialEdge edge(edges_.AllocateEdge(nts.size()));
search::PartialVertex *nt = edge.NT();
for (size_t i = 0; i < phrase.GetSize(); ++i) {
const Word &word = phrase.GetWord(i);
if (word.IsNonTerminal()) {
*(nt++) = vertices[align[i]];
words.push_back(search::kNonTerminal);
} else {
words.push_back(Convert(word));
}
}
edge.SetScore(phrase.GetFutureScore() + below_score);
// prob and oov were already accounted for.
search::ScoreRule(context_.LanguageModel(), words, edge.Between());
search::Note note;
note.vp = &phrase;
edge.SetNote(note);
edge.SetRange(range);
edges_.AddEdge(edge);
}
}
template <class Model> void Fill<Model>::AddPhraseOOV(TargetPhrase &phrase, std::list<TargetPhraseCollection*> &, const WordsRange &range)
{
std::vector<lm::WordIndex> words;
UTIL_THROW_IF2(phrase.GetSize() > 1,
"OOV target phrase should be 0 or 1 word in length");
if (phrase.GetSize())
words.push_back(Convert(phrase.GetWord(0)));
search::PartialEdge edge(edges_.AllocateEdge(0));
// Appears to be a bug that FutureScore does not already include language model.
search::ScoreRuleRet scored(search::ScoreRule(context_.LanguageModel(), words, edge.Between()));
edge.SetScore(phrase.GetFutureScore() + scored.prob * context_.LMWeight() + static_cast<search::Score>(scored.oov) * oov_weight_);
search::Note note;
note.vp = &phrase;
edge.SetNote(note);
edge.SetRange(range);
edges_.AddEdge(edge);
}
// for pruning
template <class Model> float Fill<Model>::GetBestScore(const ChartCellLabel *chartCell) const
{
search::PartialVertex vertex = chartCell->GetStack().incr->RootAlternate();
UTIL_THROW_IF2(vertex.Empty(), "hypothesis with empty stack");
return vertex.Bound();
}
// TODO: factors (but chart doesn't seem to support factors anyway).
template <class Model> lm::WordIndex Fill<Model>::Convert(const Word &word) const
{
std::size_t factor = word.GetFactor(0)->GetId();
return (factor >= vocab_mapping_.size() ? 0 : vocab_mapping_[factor]);
}
struct ChartCellBaseFactory {
ChartCellBase *operator()(size_t startPos, size_t endPos) const {
return new ChartCellBase(startPos, endPos);
}
};
} // namespace
Manager::Manager(const InputType &source) :
BaseManager(source),
cells_(source, ChartCellBaseFactory()),
parser_(source, cells_),
n_best_(search::NBestConfig(StaticData::Instance().GetNBestSize())) {}
Manager::~Manager()
{
}
template <class Model, class Best> search::History Manager::PopulateBest(const Model &model, const std::vector<lm::WordIndex> &words, Best &out)
{
const LanguageModel &abstract = LanguageModel::GetFirstLM();
const float oov_weight = abstract.OOVFeatureEnabled() ? abstract.GetOOVWeight() : 0.0;
const StaticData &data = StaticData::Instance();
search::Config config(abstract.GetWeight() * M_LN10, data.GetCubePruningPopLimit(), search::NBestConfig(data.GetNBestSize()));
search::Context<Model> context(config, model);
size_t size = m_source.GetSize();
boost::object_pool<search::Vertex> vertex_pool(std::max<size_t>(size * size / 2, 32));
for (int startPos = size-1; startPos >= 0; --startPos) {
for (size_t width = 1; width <= size-startPos; ++width) {
// full range uses RootSearch
if (startPos == 0 && startPos + width == size) {
break;
}
WordsRange range(startPos, startPos + width - 1);
Fill<Model> filler(context, words, oov_weight);
parser_.Create(range, filler);
filler.Search(out, cells_.MutableBase(range).MutableTargetLabelSet(), vertex_pool);
}
}
WordsRange range(0, size - 1);
Fill<Model> filler(context, words, oov_weight);
parser_.Create(range, filler);
return filler.RootSearch(out);
}
template <class Model> void Manager::LMCallback(const Model &model, const std::vector<lm::WordIndex> &words)
{
std::size_t nbest = StaticData::Instance().GetNBestSize();
if (nbest <= 1) {
search::History ret = PopulateBest(model, words, single_best_);
if (ret) {
backing_for_single_.resize(1);
backing_for_single_[0] = search::Applied(ret);
} else {
backing_for_single_.clear();
}
completed_nbest_ = &backing_for_single_;
} else {
search::History ret = PopulateBest(model, words, n_best_);
if (ret) {
completed_nbest_ = &n_best_.Extract(ret);
} else {
backing_for_single_.clear();
completed_nbest_ = &backing_for_single_;
}
}
}
template void Manager::LMCallback<lm::ngram::ProbingModel>(const lm::ngram::ProbingModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::RestProbingModel>(const lm::ngram::RestProbingModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::TrieModel>(const lm::ngram::TrieModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::QuantTrieModel>(const lm::ngram::QuantTrieModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::ArrayTrieModel>(const lm::ngram::ArrayTrieModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::QuantArrayTrieModel>(const lm::ngram::QuantArrayTrieModel &model, const std::vector<lm::WordIndex> &words);
void Manager::Decode()
{
LanguageModel::GetFirstLM().IncrementalCallback(*this);
}
const std::vector<search::Applied> &Manager::GetNBest() const
{
return *completed_nbest_;
}
void Manager::OutputBest(OutputCollector *collector) const
{
const long translationId = m_source.GetTranslationId();
const std::vector<search::Applied> &nbest = GetNBest();
if (!nbest.empty()) {
OutputBestHypo(collector, nbest[0], translationId);
} else {
OutputBestNone(collector, translationId);
}
}
void Manager::OutputNBest(OutputCollector *collector) const
{
if (collector == NULL) {
return;
}
OutputNBestList(collector, *completed_nbest_, m_source.GetTranslationId());
}
void Manager::OutputNBestList(OutputCollector *collector, const std::vector<search::Applied> &nbest, long translationId) const
{
const StaticData &staticData = StaticData::Instance();
const std::vector<Moses::FactorType> &outputFactorOrder = staticData.GetOutputFactorOrder();
std::ostringstream out;
// wtf? copied from the original OutputNBestList
if (collector->OutputIsCout()) {
FixPrecision(out);
}
Phrase outputPhrase;
ScoreComponentCollection features;
for (std::vector<search::Applied>::const_iterator i = nbest.begin(); i != nbest.end(); ++i) {
Incremental::PhraseAndFeatures(*i, outputPhrase, features);
// <s> and </s>
UTIL_THROW_IF2(outputPhrase.GetSize() < 2,
"Output phrase should have contained at least 2 words (beginning and end-of-sentence)");
outputPhrase.RemoveWord(0);
outputPhrase.RemoveWord(outputPhrase.GetSize() - 1);
out << translationId << " ||| ";
OutputSurface(out, outputPhrase, outputFactorOrder, false);
out << " ||| ";
features.OutputAllFeatureScores(out);
out << " ||| " << i->GetScore() << '\n';
}
out << std::flush;
assert(collector);
collector->Write(translationId, out.str());
}
void Manager::OutputDetailedTranslationReport(OutputCollector *collector) const
{
if (collector && !completed_nbest_->empty()) {
const search::Applied &applied = completed_nbest_->at(0);
OutputDetailedTranslationReport(collector,
&applied,
static_cast<const Sentence&>(m_source),
m_source.GetTranslationId());
}
}
void Manager::OutputDetailedTranslationReport(
OutputCollector *collector,
const search::Applied *applied,
const Sentence &sentence,
long translationId) const
{
if (applied == NULL) {
return;
}
std::ostringstream out;
ApplicationContext applicationContext;
OutputTranslationOptions(out, applicationContext, applied, sentence, translationId);
collector->Write(translationId, out.str());
}
void Manager::OutputTranslationOptions(std::ostream &out,
ApplicationContext &applicationContext,
const search::Applied *applied,
const Sentence &sentence, long translationId) const
{
if (applied != NULL) {
OutputTranslationOption(out, applicationContext, applied, sentence, translationId);
out << std::endl;
}
// recursive
const search::Applied *child = applied->Children();
for (size_t i = 0; i < applied->GetArity(); i++) {
OutputTranslationOptions(out, applicationContext, child++, sentence, translationId);
}
}
void Manager::OutputTranslationOption(std::ostream &out,
ApplicationContext &applicationContext,
const search::Applied *applied,
const Sentence &sentence,
long translationId) const
{
ReconstructApplicationContext(applied, sentence, applicationContext);
const TargetPhrase &phrase = *static_cast<const TargetPhrase*>(applied->GetNote().vp);
out << "Trans Opt " << translationId
<< " " << applied->GetRange()
<< ": ";
WriteApplicationContext(out, applicationContext);
out << ": " << phrase.GetTargetLHS()
<< "->" << phrase
<< " " << applied->GetScore(); // << hypo->GetScoreBreakdown() TODO: missing in incremental search hypothesis
}
// Given a hypothesis and sentence, reconstructs the 'application context' --
// the source RHS symbols of the SCFG rule that was applied, plus their spans.
void Manager::ReconstructApplicationContext(const search::Applied *applied,
const Sentence &sentence,
ApplicationContext &context) const
{
context.clear();
const WordsRange &span = applied->GetRange();
const search::Applied *child = applied->Children();
size_t i = span.GetStartPos();
size_t j = 0;
while (i <= span.GetEndPos()) {
if (j == applied->GetArity() || i < child->GetRange().GetStartPos()) {
// Symbol is a terminal.
const Word &symbol = sentence.GetWord(i);
context.push_back(std::make_pair(symbol, WordsRange(i, i)));
++i;
} else {
// Symbol is a non-terminal.
const Word &symbol = static_cast<const TargetPhrase*>(child->GetNote().vp)->GetTargetLHS();
const WordsRange &range = child->GetRange();
context.push_back(std::make_pair(symbol, range));
i = range.GetEndPos()+1;
++child;
++j;
}
}
}
void Manager::OutputDetailedTreeFragmentsTranslationReport(OutputCollector *collector) const
{
if (collector == NULL || Completed().empty()) {
return;
}
const search::Applied *applied = &Completed()[0];
const Sentence &sentence = dynamic_cast<const Sentence &>(m_source);
const size_t translationId = m_source.GetTranslationId();
std::ostringstream out;
ApplicationContext applicationContext;
OutputTreeFragmentsTranslationOptions(out, applicationContext, applied, sentence, translationId);
//Tree of full sentence
//TODO: incremental search doesn't support stateful features
collector->Write(translationId, out.str());
}
void Manager::OutputTreeFragmentsTranslationOptions(std::ostream &out,
ApplicationContext &applicationContext,
const search::Applied *applied,
const Sentence &sentence,
long translationId) const
{
if (applied != NULL) {
OutputTranslationOption(out, applicationContext, applied, sentence, translationId);
const TargetPhrase &currTarPhr = *static_cast<const TargetPhrase*>(applied->GetNote().vp);
out << " ||| ";
if (const PhraseProperty *property = currTarPhr.GetProperty("Tree")) {
out << " " << *property->GetValueString();
} else {
out << " " << "noTreeInfo";
}
out << std::endl;
}
// recursive
const search::Applied *child = applied->Children();
for (size_t i = 0; i < applied->GetArity(); i++) {
OutputTreeFragmentsTranslationOptions(out, applicationContext, child++, sentence, translationId);
}
}
void Manager::OutputBestHypo(OutputCollector *collector, search::Applied applied, long translationId) const
{
if (collector == NULL) return;
std::ostringstream out;
FixPrecision(out);
if (StaticData::Instance().GetOutputHypoScore()) {
out << applied.GetScore() << ' ';
}
Phrase outPhrase;
Incremental::ToPhrase(applied, outPhrase);
// delete 1st & last
UTIL_THROW_IF2(outPhrase.GetSize() < 2,
"Output phrase should have contained at least 2 words (beginning and end-of-sentence)");
outPhrase.RemoveWord(0);
outPhrase.RemoveWord(outPhrase.GetSize() - 1);
out << outPhrase.GetStringRep(StaticData::Instance().GetOutputFactorOrder());
out << '\n';
collector->Write(translationId, out.str());
VERBOSE(1,"BEST TRANSLATION: " << outPhrase << "[total=" << applied.GetScore() << "]" << std::endl);
}
void Manager::OutputBestNone(OutputCollector *collector, long translationId) const
{
if (collector == NULL) return;
if (StaticData::Instance().GetOutputHypoScore()) {
collector->Write(translationId, "0 \n");
} else {
collector->Write(translationId, "\n");
}
}
namespace
{
struct NoOp {
void operator()(const TargetPhrase &) const {}
};
struct AccumScore {
AccumScore(ScoreComponentCollection &out) : out_(&out) {}
void operator()(const TargetPhrase &phrase) {
out_->PlusEquals(phrase.GetScoreBreakdown());
}
ScoreComponentCollection *out_;
};
template <class Action> void AppendToPhrase(const search::Applied final, Phrase &out, Action action)
{
assert(final.Valid());
const TargetPhrase &phrase = *static_cast<const TargetPhrase*>(final.GetNote().vp);
action(phrase);
const search::Applied *child = final.Children();
for (std::size_t i = 0; i < phrase.GetSize(); ++i) {
const Word &word = phrase.GetWord(i);
if (word.IsNonTerminal()) {
AppendToPhrase(*child++, out, action);
} else {
out.AddWord(word);
}
}
}
} // namespace
void ToPhrase(const search::Applied final, Phrase &out)
{
out.Clear();
AppendToPhrase(final, out, NoOp());
}
void PhraseAndFeatures(const search::Applied final, Phrase &phrase, ScoreComponentCollection &features)
{
phrase.Clear();
features.ZeroAll();
AppendToPhrase(final, phrase, AccumScore(features));
// If we made it this far, there is only one language model.
float full, ignored_ngram;
std::size_t ignored_oov;
const LanguageModel &model = LanguageModel::GetFirstLM();
model.CalcScore(phrase, full, ignored_ngram, ignored_oov);
// CalcScore transforms, but EvaluateWhenApplied doesn't.
features.Assign(&model, full);
}
} // namespace Incremental
} // namespace Moses
|