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
|
#ifndef _DTRAIN_PAIRSAMPLING_H_
#define _DTRAIN_PAIRSAMPLING_H_
namespace dtrain
{
bool
accept_pair(score_t a, score_t b, score_t threshold)
{
if (fabs(a - b) < threshold) return false;
return true;
}
inline void
all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, float _unused = 1)
{
for (unsigned i = 0; i < s->size()-1; i++) {
for (unsigned j = i+1; j < s->size(); j++) {
if (threshold > 0) {
if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) {
training.push_back(make_pair((*s)[i], (*s)[j]));
}
} else {
training.push_back(make_pair((*s)[i], (*s)[j]));
}
}
}
}
/*
* multipartite ranking
* sort by bleu
* compare top 10% to middle 80% and low 10%
* cmp middle 80% to low 10%
*/
bool
_XYX_cmp_hyp_by_score(ScoredHyp a, ScoredHyp b)
{
return a.score < b.score;
}
inline void
partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, float hi_lo)
{
sort(s->begin(), s->end(), _XYX_cmp_hyp_by_score);
unsigned sz = s->size();
unsigned sep = round(sz*hi_lo);
for (unsigned i = 0; i < sep; i++) {
for (unsigned j = sep; j < sz; j++) {
if ((*s)[i].rank < (*s)[j].rank) {
if (threshold > 0) {
if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) {
training.push_back(make_pair((*s)[i], (*s)[j]));
}
} else {
training.push_back(make_pair((*s)[i], (*s)[j]));
}
}
}
}
for (unsigned i = sep; i < sz-sep; i++) {
for (unsigned j = sz-sep; j < sz; j++) {
if ((*s)[i].rank < (*s)[j].rank) {
if (threshold > 0) {
if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) {
training.push_back(make_pair((*s)[i], (*s)[j]));
}
} else {
training.push_back(make_pair((*s)[i], (*s)[j]));
}
}
}
}
}
/*
* pair sampling as in
* 'Tuning as Ranking' (Hopkins & May, 2011)
* count = 5000
* threshold = 5% BLEU (0.05 for param 3)
* cut = top 50
*/
bool
_PRO_cmp_pair_by_diff(pair<ScoredHyp,ScoredHyp> a, pair<ScoredHyp,ScoredHyp> b)
{
// descending order
return (fabs(a.first.score - a.second.score)) > (fabs(b.first.score - b.second.score));
}
inline void
PROsampling(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, float _unused = 1)
{
unsigned max_count = 5000, count = 0;
bool b = false;
for (unsigned i = 0; i < s->size()-1; i++) {
for (unsigned j = i+1; j < s->size(); j++) {
if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) {
training.push_back(make_pair((*s)[i], (*s)[j]));
if (++count == max_count) {
b = true;
break;
}
}
}
if (b) break;
}
if (training.size() > 50) {
sort(training.begin(), training.end(), _PRO_cmp_pair_by_diff);
training.erase(training.begin()+50, training.end());
}
return;
}
} // namespace
#endif
|
