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
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
|
// $Id$
// vim:tabstop=2
/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2006 University of Edinburgh
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
***********************************************************************/
#include <iostream>
#include <limits>
#include <vector>
#include <algorithm>
#include "TranslationOption.h"
#include "TranslationOptionCollection.h"
#include "Hypothesis.h"
#include "Util.h"
#include "SquareMatrix.h"
#include "StaticData.h"
#include "InputType.h"
#include "Manager.h"
#include "IOWrapper.h"
#include "moses/FF/FFState.h"
#include "moses/FF/StatefulFeatureFunction.h"
#include "moses/FF/StatelessFeatureFunction.h"
#include <boost/foreach.hpp>
using namespace std;
namespace Moses
{
#ifdef USE_HYPO_POOL
ObjectPool<Hypothesis> Hypothesis::s_objectPool("Hypothesis", 300000);
#endif
Hypothesis::
Hypothesis(Manager& manager, InputType const& source, const TranslationOption &initialTransOpt)
: m_prevHypo(NULL)
, m_sourceCompleted(source.GetSize(), manager.GetSource().m_sourceCompleted)
, m_sourceInput(source)
, m_currSourceWordsRange(
m_sourceCompleted.GetFirstGapPos()>0 ? 0 : NOT_FOUND,
m_sourceCompleted.GetFirstGapPos()>0 ? m_sourceCompleted.GetFirstGapPos()-1 : NOT_FOUND)
, m_currTargetWordsRange(NOT_FOUND, NOT_FOUND)
, m_wordDeleted(false)
, m_totalScore(0.0f)
, m_futureScore(0.0f)
, m_ffStates(StatefulFeatureFunction::GetStatefulFeatureFunctions().size())
, m_arcList(NULL)
, m_transOpt(initialTransOpt)
, m_manager(manager)
, m_id(m_manager.GetNextHypoId())
{
// used for initial seeding of trans process
// initialize scores
//_hash_computed = false;
//s_HypothesesCreated = 1;
const vector<const StatefulFeatureFunction*>& ffs = StatefulFeatureFunction::GetStatefulFeatureFunctions();
for (unsigned i = 0; i < ffs.size(); ++i)
m_ffStates[i] = ffs[i]->EmptyHypothesisState(source);
m_manager.GetSentenceStats().AddCreated();
}
/***
* continue prevHypo by appending the phrases in transOpt
*/
Hypothesis::
Hypothesis(const Hypothesis &prevHypo, const TranslationOption &transOpt)
: m_prevHypo(&prevHypo)
, m_sourceCompleted(prevHypo.m_sourceCompleted )
, m_sourceInput(prevHypo.m_sourceInput)
, m_currSourceWordsRange(transOpt.GetSourceWordsRange())
, m_currTargetWordsRange(prevHypo.m_currTargetWordsRange.GetEndPos() + 1,
prevHypo.m_currTargetWordsRange.GetEndPos()
+ transOpt.GetTargetPhrase().GetSize())
, m_wordDeleted(false)
, m_totalScore(0.0f)
, m_futureScore(0.0f)
, m_ffStates(prevHypo.m_ffStates.size())
, m_arcList(NULL)
, m_transOpt(transOpt)
, m_manager(prevHypo.GetManager())
, m_id(m_manager.GetNextHypoId())
{
m_currScoreBreakdown.PlusEquals(transOpt.GetScoreBreakdown());
// assert that we are not extending our hypothesis by retranslating something
// that this hypothesis has already translated!
assert(!m_sourceCompleted.Overlap(m_currSourceWordsRange));
//_hash_computed = false;
m_sourceCompleted.SetValue(m_currSourceWordsRange.GetStartPos(), m_currSourceWordsRange.GetEndPos(), true);
m_wordDeleted = transOpt.IsDeletionOption();
m_manager.GetSentenceStats().AddCreated();
}
Hypothesis::
~Hypothesis()
{
for (unsigned i = 0; i < m_ffStates.size(); ++i)
delete m_ffStates[i];
if (m_arcList) {
ArcList::iterator iter;
for (iter = m_arcList->begin() ; iter != m_arcList->end() ; ++iter) {
FREEHYPO(*iter);
}
m_arcList->clear();
delete m_arcList;
m_arcList = NULL;
}
}
void
Hypothesis::
AddArc(Hypothesis *loserHypo)
{
if (!m_arcList) {
if (loserHypo->m_arcList) { // we don't have an arcList, but loser does
this->m_arcList = loserHypo->m_arcList; // take ownership, we'll delete
loserHypo->m_arcList = 0; // prevent a double deletion
} else {
this->m_arcList = new ArcList();
}
} else {
if (loserHypo->m_arcList) { // both have an arc list: merge. delete loser
size_t my_size = m_arcList->size();
size_t add_size = loserHypo->m_arcList->size();
this->m_arcList->resize(my_size + add_size, 0);
std::memcpy(&(*m_arcList)[0] + my_size, &(*loserHypo->m_arcList)[0], add_size * sizeof(Hypothesis *));
delete loserHypo->m_arcList;
loserHypo->m_arcList = 0;
} else { // loserHypo doesn't have any arcs
// DO NOTHING
}
}
m_arcList->push_back(loserHypo);
}
/***
* return the subclass of Hypothesis most appropriate to the given translation option
*/
Hypothesis*
Hypothesis::
CreateNext(const TranslationOption &transOpt) const
{
return Create(*this, transOpt);
}
/***
* return the subclass of Hypothesis most appropriate to the given translation option
*/
Hypothesis*
Hypothesis::
Create(const Hypothesis &prevHypo, const TranslationOption &transOpt)
{
#ifdef USE_HYPO_POOL
Hypothesis *ptr = s_objectPool.getPtr();
return new(ptr) Hypothesis(prevHypo, transOpt);
#else
return new Hypothesis(prevHypo, transOpt);
#endif
}
/***
* return the subclass of Hypothesis most appropriate to the given target phrase
*/
Hypothesis*
Hypothesis::
Create(Manager& manager, InputType const& m_source,
const TranslationOption &initialTransOpt)
{
#ifdef USE_HYPO_POOL
Hypothesis *ptr = s_objectPool.getPtr();
return new(ptr) Hypothesis(manager, m_source, initialTransOpt);
#else
return new Hypothesis(manager, m_source, initialTransOpt);
#endif
}
/** check, if two hypothesis can be recombined.
this is actually a sorting function that allows us to
keep an ordered list of hypotheses. This makes recombination
much quicker.
*/
int
Hypothesis::
RecombineCompare(const Hypothesis &compare) const
{
// -1 = this < compare
// +1 = this > compare
// 0 = this ==compare
int comp = m_sourceCompleted.Compare(compare.m_sourceCompleted);
if (comp != 0)
return comp;
for (unsigned i = 0; i < m_ffStates.size(); ++i) {
if (m_ffStates[i] == NULL || compare.m_ffStates[i] == NULL) {
comp = m_ffStates[i] - compare.m_ffStates[i];
} else {
comp = m_ffStates[i]->Compare(*compare.m_ffStates[i]);
}
if (comp != 0) return comp;
}
return 0;
}
void
Hypothesis::
EvaluateWhenApplied(StatefulFeatureFunction const& sfff,
int state_idx)
{
const StaticData &staticData = StaticData::Instance();
if (! staticData.IsFeatureFunctionIgnored( sfff ))
{
m_ffStates[state_idx]
= sfff.EvaluateWhenApplied
(*this, m_prevHypo ? m_prevHypo->m_ffStates[state_idx] : NULL,
&m_currScoreBreakdown);
}
}
void
Hypothesis::
EvaluateWhenApplied(const StatelessFeatureFunction& slff)
{
const StaticData &staticData = StaticData::Instance();
if (! staticData.IsFeatureFunctionIgnored( slff )) {
slff.EvaluateWhenApplied(*this, &m_currScoreBreakdown);
}
}
/***
* calculate the logarithm of our total translation score (sum up components)
*/
void
Hypothesis::
EvaluateWhenApplied(const SquareMatrix &futureScore)
{
IFVERBOSE(2) {
m_manager.GetSentenceStats().StartTimeOtherScore();
}
// some stateless score producers cache their values in the translation
// option: add these here
// language model scores for n-grams completely contained within a target
// phrase are also included here
// compute values of stateless feature functions that were not
// cached in the translation option
const vector<const StatelessFeatureFunction*>& sfs =
StatelessFeatureFunction::GetStatelessFeatureFunctions();
for (unsigned i = 0; i < sfs.size(); ++i) {
const StatelessFeatureFunction &ff = *sfs[i];
EvaluateWhenApplied(ff);
}
const vector<const StatefulFeatureFunction*>& ffs =
StatefulFeatureFunction::GetStatefulFeatureFunctions();
for (unsigned i = 0; i < ffs.size(); ++i) {
const StatefulFeatureFunction &ff = *ffs[i];
const StaticData &staticData = StaticData::Instance();
if (! staticData.IsFeatureFunctionIgnored(ff)) {
m_ffStates[i] = ff.EvaluateWhenApplied(*this,
m_prevHypo ? m_prevHypo->m_ffStates[i] : NULL,
&m_currScoreBreakdown);
}
}
IFVERBOSE(2) {
m_manager.GetSentenceStats().StopTimeOtherScore();
m_manager.GetSentenceStats().StartTimeEstimateScore();
}
// FUTURE COST
m_futureScore = futureScore.CalcFutureScore( m_sourceCompleted );
// TOTAL
m_totalScore = m_currScoreBreakdown.GetWeightedScore() + m_futureScore;
if (m_prevHypo) m_totalScore += m_prevHypo->GetScore();
IFVERBOSE(2) {
m_manager.GetSentenceStats().StopTimeEstimateScore();
}
}
const Hypothesis* Hypothesis::GetPrevHypo()const
{
return m_prevHypo;
}
/**
* print hypothesis information for pharaoh-style logging
*/
void
Hypothesis::
PrintHypothesis() const
{
if (!m_prevHypo) {
TRACE_ERR(endl << "NULL hypo" << endl);
return;
}
TRACE_ERR(endl << "creating hypothesis "<< m_id <<" from "<< m_prevHypo->m_id<<" ( ");
int end = (int)(m_prevHypo->GetCurrTargetPhrase().GetSize()-1);
int start = end-1;
if ( start < 0 ) start = 0;
if ( m_prevHypo->m_currTargetWordsRange.GetStartPos() == NOT_FOUND ) {
TRACE_ERR( "<s> ");
} else {
TRACE_ERR( "... ");
}
if (end>=0) {
WordsRange range(start, end);
TRACE_ERR( m_prevHypo->GetCurrTargetPhrase().GetSubString(range) << " ");
}
TRACE_ERR( ")"<<endl);
TRACE_ERR( "\tbase score "<< (m_prevHypo->m_totalScore - m_prevHypo->m_futureScore) <<endl);
TRACE_ERR( "\tcovering "<<m_currSourceWordsRange.GetStartPos()<<"-"<<m_currSourceWordsRange.GetEndPos()
<<": " << m_transOpt.GetInputPath().GetPhrase() << endl);
TRACE_ERR( "\ttranslated as: "<<(Phrase&) GetCurrTargetPhrase()<<endl); // <<" => translation cost "<<m_score[ScoreType::PhraseTrans];
if (m_wordDeleted) TRACE_ERR( "\tword deleted"<<endl);
// TRACE_ERR( "\tdistance: "<<GetCurrSourceWordsRange().CalcDistortion(m_prevHypo->GetCurrSourceWordsRange())); // << " => distortion cost "<<(m_score[ScoreType::Distortion]*weightDistortion)<<endl;
// TRACE_ERR( "\tlanguage model cost "); // <<m_score[ScoreType::LanguageModelScore]<<endl;
// TRACE_ERR( "\tword penalty "); // <<(m_score[ScoreType::WordPenalty]*weightWordPenalty)<<endl;
TRACE_ERR( "\tscore "<<m_totalScore - m_futureScore<<" + future cost "<<m_futureScore<<" = "<<m_totalScore<<endl);
TRACE_ERR( "\tunweighted feature scores: " << m_currScoreBreakdown << endl);
//PrintLMScores();
}
void
Hypothesis::
CleanupArcList()
{
// point this hypo's main hypo to itself
SetWinningHypo(this);
if (!m_arcList) return;
/* keep only number of arcs we need to create all n-best paths.
* However, may not be enough if only unique candidates are needed,
* so we'll keep all of arc list if nedd distinct n-best list
*/
const StaticData &staticData = StaticData::Instance();
size_t nBestSize = staticData.GetNBestSize();
bool distinctNBest = (staticData.GetDistinctNBest() ||
staticData.GetLatticeSamplesSize() ||
staticData.UseMBR() ||
staticData.GetOutputSearchGraph() ||
staticData.GetOutputSearchGraphSLF() ||
staticData.GetOutputSearchGraphHypergraph() ||
staticData.UseLatticeMBR());
if (!distinctNBest && m_arcList->size() > nBestSize * 5)
{
// prune arc list only if there too many arcs
NTH_ELEMENT4(m_arcList->begin(), m_arcList->begin() + nBestSize - 1,
m_arcList->end(), CompareHypothesisTotalScore());
// delete bad ones
ArcList::iterator iter;
for (iter = m_arcList->begin() + nBestSize; iter != m_arcList->end() ; ++iter)
FREEHYPO(*iter);
m_arcList->erase(m_arcList->begin() + nBestSize, m_arcList->end());
}
// set all arc's main hypo variable to this hypo
ArcList::iterator iter = m_arcList->begin();
for (; iter != m_arcList->end() ; ++iter) {
Hypothesis *arc = *iter;
arc->SetWinningHypo(this);
}
}
TargetPhrase const&
Hypothesis::
GetCurrTargetPhrase() const
{ return m_transOpt.GetTargetPhrase(); }
void
Hypothesis::
GetOutputPhrase(Phrase &out) const
{
if (m_prevHypo != NULL)
m_prevHypo->GetOutputPhrase(out);
out.Append(GetCurrTargetPhrase());
}
TO_STRING_BODY(Hypothesis)
// friend
ostream& operator<<(ostream& out, const Hypothesis& hypo)
{
hypo.ToStream(out);
// words bitmap
out << "[" << hypo.m_sourceCompleted << "] ";
// scores
out << " [total=" << hypo.GetTotalScore() << "]";
out << " " << hypo.GetScoreBreakdown();
// alignment
out << " " << hypo.GetCurrTargetPhrase().GetAlignNonTerm();
return out;
}
std::string
Hypothesis::
GetSourcePhraseStringRep(const vector<FactorType> factorsToPrint) const
{ return m_transOpt.GetInputPath().GetPhrase().GetStringRep(factorsToPrint); }
std::string
Hypothesis::
GetTargetPhraseStringRep(const vector<FactorType> factorsToPrint) const
{ return (m_prevHypo ? GetCurrTargetPhrase().GetStringRep(factorsToPrint) : ""); }
std::string
Hypothesis::
GetSourcePhraseStringRep() const
{
vector<FactorType> allFactors(MAX_NUM_FACTORS);
for(size_t i=0; i < MAX_NUM_FACTORS; i++)
allFactors[i] = i;
return GetSourcePhraseStringRep(allFactors);
}
std::string
Hypothesis::
GetTargetPhraseStringRep() const
{
vector<FactorType> allFactors(MAX_NUM_FACTORS);
for(size_t i=0; i < MAX_NUM_FACTORS; i++)
allFactors[i] = i;
return GetTargetPhraseStringRep(allFactors);
}
void
Hypothesis::
OutputAlignment(std::ostream &out) const
{
std::vector<const Hypothesis *> edges;
const Hypothesis *currentHypo = this;
while (currentHypo) {
edges.push_back(currentHypo);
currentHypo = currentHypo->GetPrevHypo();
}
OutputAlignment(out, edges);
}
void
Hypothesis::
OutputAlignment(ostream &out, const vector<const Hypothesis *> &edges)
{
size_t targetOffset = 0;
for (int currEdge = (int)edges.size() - 1 ; currEdge >= 0 ; currEdge--) {
const Hypothesis &edge = *edges[currEdge];
const TargetPhrase &tp = edge.GetCurrTargetPhrase();
size_t sourceOffset = edge.GetCurrSourceWordsRange().GetStartPos();
OutputAlignment(out, tp.GetAlignTerm(), sourceOffset, targetOffset);
targetOffset += tp.GetSize();
}
// Removing std::endl here breaks -alignment-output-file, so stop doing that, please :)
// Or fix it somewhere else.
out << std::endl;
}
void
Hypothesis::
OutputAlignment(ostream &out, const AlignmentInfo &ai,
size_t sourceOffset, size_t targetOffset)
{
typedef std::vector< const std::pair<size_t,size_t>* > AlignVec;
AlignVec alignments = ai.GetSortedAlignments();
AlignVec::const_iterator it;
for (it = alignments.begin(); it != alignments.end(); ++it) {
const std::pair<size_t,size_t> &alignment = **it;
out << alignment.first + sourceOffset << "-" << alignment.second + targetOffset << " ";
}
}
void
Hypothesis::
OutputInput(std::vector<const Phrase*>& map, const Hypothesis* hypo)
{
if (!hypo->GetPrevHypo()) return;
OutputInput(map, hypo->GetPrevHypo());
map[hypo->GetCurrSourceWordsRange().GetStartPos()]
= &hypo->GetTranslationOption().GetInputPath().GetPhrase();
}
void
Hypothesis::
OutputInput(std::ostream& os) const
{
size_t len = this->GetInput().GetSize();
std::vector<const Phrase*> inp_phrases(len, 0);
OutputInput(inp_phrases, this);
for (size_t i=0; i<len; ++i)
if (inp_phrases[i]) os << *inp_phrases[i];
}
void
Hypothesis::
OutputBestSurface(std::ostream &out, const std::vector<FactorType> &outputFactorOrder,
char reportSegmentation, bool reportAllFactors) const
{
if (m_prevHypo)
{ // recursively retrace this best path through the lattice, starting from the end of the hypothesis sentence
m_prevHypo->OutputBestSurface(out, outputFactorOrder, reportSegmentation, reportAllFactors);
}
OutputSurface(out, *this, outputFactorOrder, reportSegmentation, reportAllFactors);
}
//////////////////////////////////////////////////////////////////////////
/***
* print surface factor only for the given phrase
*/
void
Hypothesis::
OutputSurface(std::ostream &out, const Hypothesis &edge,
const std::vector<FactorType> &outputFactorOrder,
char reportSegmentation, bool reportAllFactors) const
{
UTIL_THROW_IF2(outputFactorOrder.size() == 0,
"Must specific at least 1 output factor");
const TargetPhrase& phrase = edge.GetCurrTargetPhrase();
bool markUnknown = StaticData::Instance().GetMarkUnknown();
if (reportAllFactors == true) {
out << phrase;
} else {
FactorType placeholderFactor = StaticData::Instance().GetPlaceholderFactor();
std::map<size_t, const Factor*> placeholders;
if (placeholderFactor != NOT_FOUND) {
// creates map of target position -> factor for placeholders
placeholders = GetPlaceholders(edge, placeholderFactor);
}
size_t size = phrase.GetSize();
for (size_t pos = 0 ; pos < size ; pos++) {
const Factor *factor = phrase.GetFactor(pos, outputFactorOrder[0]);
if (placeholders.size()) {
// do placeholders
std::map<size_t, const Factor*>::const_iterator iter = placeholders.find(pos);
if (iter != placeholders.end()) {
factor = iter->second;
}
}
UTIL_THROW_IF2(factor == NULL,
"No factor 0 at position " << pos);
//preface surface form with UNK if marking unknowns
const Word &word = phrase.GetWord(pos);
if(markUnknown && word.IsOOV()) {
out << "UNK" << *factor;
} else {
out << *factor;
}
for (size_t i = 1 ; i < outputFactorOrder.size() ; i++) {
const Factor *factor = phrase.GetFactor(pos, outputFactorOrder[i]);
UTIL_THROW_IF2(factor == NULL,
"No factor " << i << " at position " << pos);
out << "|" << *factor;
}
out << " ";
}
}
// trace ("report segmentation") option "-t" / "-tt"
if (reportSegmentation > 0 && phrase.GetSize() > 0) {
const WordsRange &sourceRange = edge.GetCurrSourceWordsRange();
const int sourceStart = sourceRange.GetStartPos();
const int sourceEnd = sourceRange.GetEndPos();
out << "|" << sourceStart << "-" << sourceEnd; // enriched "-tt"
if (reportSegmentation == 2) {
out << ",wa=";
const AlignmentInfo &ai = edge.GetCurrTargetPhrase().GetAlignTerm();
Hypothesis::OutputAlignment(out, ai, 0, 0);
out << ",total=";
out << edge.GetScore() - edge.GetPrevHypo()->GetScore();
out << ",";
ScoreComponentCollection scoreBreakdown(edge.GetScoreBreakdown());
scoreBreakdown.MinusEquals(edge.GetPrevHypo()->GetScoreBreakdown());
scoreBreakdown.OutputAllFeatureScores(out);
}
out << "| ";
}
}
std::map<size_t, const Factor*>
Hypothesis::
GetPlaceholders(const Hypothesis &hypo, FactorType placeholderFactor) const
{
const InputPath &inputPath = hypo.GetTranslationOption().GetInputPath();
const Phrase &inputPhrase = inputPath.GetPhrase();
std::map<size_t, const Factor*> ret;
for (size_t sourcePos = 0; sourcePos < inputPhrase.GetSize(); ++sourcePos) {
const Factor *factor = inputPhrase.GetFactor(sourcePos, placeholderFactor);
if (factor) {
std::set<size_t> targetPos = hypo.GetTranslationOption().GetTargetPhrase().GetAlignTerm().GetAlignmentsForSource(sourcePos);
UTIL_THROW_IF2(targetPos.size() != 1,
"Placeholder should be aligned to 1, and only 1, word");
ret[*targetPos.begin()] = factor;
}
}
return ret;
}
#ifdef HAVE_XMLRPC_C
void
Hypothesis::
OutputLocalWordAlignment(vector<xmlrpc_c::value>& dest) const
{
using namespace std;
WordsRange const& src = this->GetCurrSourceWordsRange();
WordsRange const& trg = this->GetCurrTargetWordsRange();
vector<pair<size_t,size_t> const* > a
= this->GetCurrTargetPhrase().GetAlignTerm().GetSortedAlignments();
typedef pair<size_t,size_t> item;
map<string, xmlrpc_c::value> M;
BOOST_FOREACH(item const* p, a)
{
M["source-word"] = src.GetStartPosition() + p->first;
M["target-word"] = trg.GetStartPosition() + p->second;
dest.push_back(xmlrpc_c::value_struct(M));
}
}
void
Hypothesis::
OutputWordAlignment(vector<xmlrpc_c::value>& out) const
{
vector<Hypothesis const*> tmp;
for (Hypothesis const* h = this; h; h = h->GetPrevHypo())
tmp.push_back(h);
for (size_t i = tmp.size(); i-- > 0;)
tmp[i]->OutputLocalWordAlignment(out);
}
#endif
}
|