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
|
#ifndef _DTRAIN_KBESTGET_H_
#define _DTRAIN_KBESTGET_H_
#include "kbest.h" // cdec
#include "sentence_metadata.h"
#include "verbose.h"
#include "viterbi.h"
#include "ff_register.h"
#include "decoder.h"
#include "weights.h"
#include "logval.h"
using namespace std;
namespace dtrain
{
typedef double score_t;
struct ScoredHyp
{
vector<WordID> w;
SparseVector<double> f;
score_t model;
score_t score;
unsigned rank;
};
struct LocalScorer
{
unsigned N_;
vector<score_t> w_;
virtual score_t
Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned rank, const unsigned src_len)=0;
void Reset() {} // only for approx bleu
inline void
Init(unsigned N, vector<score_t> weights)
{
assert(N > 0);
N_ = N;
if (weights.empty()) for (unsigned i = 0; i < N_; i++) w_.push_back(1./N_);
else w_ = weights;
}
inline score_t
brevity_penalty(const unsigned hyp_len, const unsigned ref_len)
{
if (hyp_len > ref_len) return 1;
return exp(1 - (score_t)ref_len/hyp_len);
}
};
struct HypSampler : public DecoderObserver
{
LocalScorer* scorer_;
vector<WordID>* ref_;
unsigned f_count_, sz_;
virtual vector<ScoredHyp>* GetSamples()=0;
inline void SetScorer(LocalScorer* scorer) { scorer_ = scorer; }
inline void SetRef(vector<WordID>& ref) { ref_ = &ref; }
inline unsigned get_f_count() { return f_count_; }
inline unsigned get_sz() { return sz_; }
};
////////////////////////////////////////////////////////////////////////////////
struct KBestGetter : public HypSampler
{
const unsigned k_;
const string filter_type_;
vector<ScoredHyp> s_;
unsigned src_len_;
KBestGetter(const unsigned k, const string filter_type) :
k_(k), filter_type_(filter_type) {}
virtual void
NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
{
src_len_ = smeta.GetSourceLength();
KBestScored(*hg);
}
vector<ScoredHyp>* GetSamples() { return &s_; }
void
KBestScored(const Hypergraph& forest)
{
if (filter_type_ == "uniq") {
KBestUnique(forest);
} else if (filter_type_ == "not") {
KBestNoFilter(forest);
}
}
void
KBestUnique(const Hypergraph& forest)
{
s_.clear(); sz_ = f_count_ = 0;
KBest::KBestDerivations<vector<WordID>, ESentenceTraversal,
KBest::FilterUnique, prob_t, EdgeProb> kbest(forest, k_);
for (unsigned i = 0; i < k_; ++i) {
const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique,
prob_t, EdgeProb>::Derivation* d =
kbest.LazyKthBest(forest.nodes_.size() - 1, i);
if (!d) break;
ScoredHyp h;
h.w = d->yield;
h.f = d->feature_values;
h.model = log(d->score);
h.rank = i;
h.score = scorer_->Score(h.w, *ref_, i, src_len_);
s_.push_back(h);
sz_++;
f_count_ += h.f.size();
}
}
void
KBestNoFilter(const Hypergraph& forest)
{
s_.clear(); sz_ = f_count_ = 0;
KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, k_);
for (unsigned i = 0; i < k_; ++i) {
const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
kbest.LazyKthBest(forest.nodes_.size() - 1, i);
if (!d) break;
ScoredHyp h;
h.w = d->yield;
h.f = d->feature_values;
h.model = log(d->score);
h.rank = i;
h.score = scorer_->Score(h.w, *ref_, i, src_len_);
s_.push_back(h);
sz_++;
f_count_ += h.f.size();
}
}
};
} // namespace
#endif
|
