summaryrefslogtreecommitdiff
path: root/gi/pf/align-lexonly-pyp.cc
blob: e7509f577837e9b8674908a6531c2c37a400ab67 (plain)
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
#include <iostream>
#include <queue>

#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>

#include "tdict.h"
#include "stringlib.h"
#include "filelib.h"
#include "array2d.h"
#include "sampler.h"
#include "corpus.h"
#include "pyp_tm.h"
#include "hpyp_tm.h"
#include "quasi_model2.h"

using namespace std;
namespace po = boost::program_options;

void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
  po::options_description opts("Configuration options");
  opts.add_options()
        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
        ("infer_alignment_hyperparameters,I", "Infer alpha and p_null, otherwise fixed values will be assumed")
        ("p_null,0", po::value<double>()->default_value(0.08), "probability of aligning to null")
        ("align_alpha,a", po::value<double>()->default_value(4.0), "how 'tight' is the bias toward be along the diagonal?")
        ("input,i",po::value<string>(),"Read parallel data from")
        ("random_seed,S",po::value<uint32_t>(), "Random seed");
  po::options_description clo("Command line options");
  clo.add_options()
        ("config", po::value<string>(), "Configuration file")
        ("help,h", "Print this help message and exit");
  po::options_description dconfig_options, dcmdline_options;
  dconfig_options.add(opts);
  dcmdline_options.add(opts).add(clo);
  
  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
  if (conf->count("config")) {
    ifstream config((*conf)["config"].as<string>().c_str());
    po::store(po::parse_config_file(config, dconfig_options), *conf);
  }
  po::notify(*conf);

  if (conf->count("help") || (conf->count("input") == 0)) {
    cerr << dcmdline_options << endl;
    exit(1);
  }
}

MT19937* prng;

struct LexicalAlignment {
  unsigned char src_index;
  bool is_transliteration;
  vector<pair<short, short> > derivation;
};

struct AlignedSentencePair {
  vector<WordID> src;
  vector<WordID> trg;
  vector<LexicalAlignment> a;
  Array2D<short> posterior;
};

template <class LexicalTranslationModel>
struct Aligner {
  Aligner(const vector<vector<WordID> >& lets,
          int vocab_size,
          int num_letters,
          const po::variables_map& conf,
          vector<AlignedSentencePair>* c) :
      corpus(*c),
      paj_model(conf["align_alpha"].as<double>(), conf["p_null"].as<double>()),
      infer_paj(conf.count("infer_alignment_hyperparameters") > 0),
      model(lets, vocab_size, num_letters),
      kNULL(TD::Convert("NULL")) {
    assert(lets[kNULL].size() == 0);
  }

  vector<AlignedSentencePair>& corpus;
  QuasiModel2 paj_model;
  const bool infer_paj;
  LexicalTranslationModel model;
  const WordID kNULL;

  void ResampleHyperparameters() {
    model.ResampleHyperparameters(prng);
    if (infer_paj) paj_model.ResampleHyperparameters(prng);
  }

  void InitializeRandom() {
    cerr << "Initializing with random alignments ...\n";
    for (unsigned i = 0; i < corpus.size(); ++i) {
      AlignedSentencePair& asp = corpus[i];
      asp.a.resize(asp.trg.size());
      for (unsigned j = 0; j < asp.trg.size(); ++j) {
        unsigned char& a_j = asp.a[j].src_index;
        a_j = prng->next() * (1 + asp.src.size());
        const WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
        model.Increment(f_a_j, asp.trg[j], &*prng);
        paj_model.Increment(a_j, j, asp.src.size(), asp.trg.size());
      }
    }
    cerr << "Corpus intialized randomly." << endl;
    cerr << "LLH = " << Likelihood() << "    \t(Amodel=" << paj_model.Likelihood()
         << " TModel=" << model.Likelihood() << ") contexts=" << model.UniqueConditioningContexts() << endl;
  }

  void ResampleCorpus() {
    for (unsigned i = 0; i < corpus.size(); ++i) {
      AlignedSentencePair& asp = corpus[i];
      SampleSet<prob_t> ss; ss.resize(asp.src.size() + 1);
      for (unsigned j = 0; j < asp.trg.size(); ++j) {
        unsigned char& a_j = asp.a[j].src_index;
        const WordID e_j = asp.trg[j];
        WordID f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
        model.Decrement(f_a_j, e_j, prng);
        paj_model.Decrement(a_j, j, asp.src.size(), asp.trg.size());

        for (unsigned prop_a_j = 0; prop_a_j <= asp.src.size(); ++prop_a_j) {
          const WordID prop_f = (prop_a_j ? asp.src[prop_a_j - 1] : kNULL);
          ss[prop_a_j] = model.Prob(prop_f, e_j);
          ss[prop_a_j] *= paj_model.Prob(prop_a_j, j, asp.src.size(), asp.trg.size());
        }
        a_j = prng->SelectSample(ss);
        f_a_j = (a_j ? asp.src[a_j - 1] : kNULL);
        model.Increment(f_a_j, e_j, prng);
        paj_model.Increment(a_j, j, asp.src.size(), asp.trg.size());
      }
    }
  }

  prob_t Likelihood() const {
    return model.Likelihood() * paj_model.Likelihood();
  }
};

void ExtractLetters(const set<WordID>& v, vector<vector<WordID> >* l, set<WordID>* letset = NULL) {
  for (set<WordID>::const_iterator it = v.begin(); it != v.end(); ++it) {
    vector<WordID>& letters = (*l)[*it];
    if (letters.size()) continue;   // if e and f have the same word

    const string& w = TD::Convert(*it);
    
    size_t cur = 0;
    while (cur < w.size()) {
      const size_t len = UTF8Len(w[cur]);
      letters.push_back(TD::Convert(w.substr(cur, len)));
      if (letset) letset->insert(letters.back());
      cur += len;
    }
  }
}

void Debug(const AlignedSentencePair& asp) {
  cerr << TD::GetString(asp.src) << endl << TD::GetString(asp.trg) << endl;
  Array2D<bool> a(asp.src.size(), asp.trg.size());
  for (unsigned j = 0; j < asp.trg.size(); ++j) {
    assert(asp.a[j].src_index <= asp.src.size());
    if (asp.a[j].src_index) a(asp.a[j].src_index - 1, j) = true;
  }
  cerr << a << endl;
}

void AddSample(AlignedSentencePair* asp) {
  for (unsigned j = 0; j < asp->trg.size(); ++j)
    asp->posterior(asp->a[j].src_index, j)++;
}

void WriteAlignments(const AlignedSentencePair& asp) {
  bool first = true;
  for (unsigned j = 0; j < asp.trg.size(); ++j) {
    int src_index = -1;
    int mc = -1;
    for (unsigned i = 0; i <= asp.src.size(); ++i) {
      if (asp.posterior(i, j) > mc) {
        mc = asp.posterior(i, j);
        src_index = i;
      }
    }

    if (src_index) {
      if (first) first = false; else cout << ' ';
      cout << (src_index - 1) << '-' << j;
    }
  }
  cout << endl;
}

int main(int argc, char** argv) {
  po::variables_map conf;
  InitCommandLine(argc, argv, &conf);

  if (conf.count("random_seed"))
    prng = new MT19937(conf["random_seed"].as<uint32_t>());
  else
    prng = new MT19937;

  vector<vector<int> > corpuse, corpusf;
  set<int> vocabe, vocabf;
  corpus::ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
  cerr << "f-Corpus size: " << corpusf.size() << " sentences\n";
  cerr << "f-Vocabulary size: " << vocabf.size() << " types\n";
  cerr << "f-Corpus size: " << corpuse.size() << " sentences\n";
  cerr << "f-Vocabulary size: " << vocabe.size() << " types\n";
  assert(corpusf.size() == corpuse.size());

  vector<AlignedSentencePair> corpus(corpuse.size());
  for (unsigned i = 0; i < corpuse.size(); ++i) {
    corpus[i].src.swap(corpusf[i]);
    corpus[i].trg.swap(corpuse[i]);
    corpus[i].posterior.resize(corpus[i].src.size() + 1, corpus[i].trg.size());
  }
  corpusf.clear(); corpuse.clear();

  vocabf.insert(TD::Convert("NULL"));
  vector<vector<WordID> > letters(TD::NumWords());
  set<WordID> letset;
  ExtractLetters(vocabe, &letters, &letset);
  ExtractLetters(vocabf, &letters, NULL);
  letters[TD::Convert("NULL")].clear();

  //Aligner<PYPLexicalTranslation> aligner(letters, vocabe.size(), letset.size(), conf, &corpus);
  Aligner<HPYPLexicalTranslation> aligner(letters, vocabe.size(), letset.size(), conf, &corpus);
  aligner.InitializeRandom();

  const unsigned samples = conf["samples"].as<unsigned>();
  for (int i = 0; i < samples; ++i) {
    for (int j = 65; j < 67; ++j) Debug(corpus[j]);
    if (i % 10 == 9) {
      aligner.ResampleHyperparameters();
      cerr << "LLH = " << aligner.Likelihood() << "    \t(Amodel=" << aligner.paj_model.Likelihood()
           << " TModel=" << aligner.model.Likelihood() << ") contexts=" << aligner.model.UniqueConditioningContexts() << endl;
    }
    aligner.ResampleCorpus();
    if (i > (samples / 5) && (i % 6 == 5)) for (int j = 0; j < corpus.size(); ++j) AddSample(&corpus[j]);
  }
  for (unsigned i = 0; i < corpus.size(); ++i)
    WriteAlignments(corpus[i]);
  aligner.model.Summary();

  return 0;
}