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Diffstat (limited to 'word-aligner/fast_align.cc')
| -rw-r--r-- | word-aligner/fast_align.cc | 310 |
1 files changed, 310 insertions, 0 deletions
diff --git a/word-aligner/fast_align.cc b/word-aligner/fast_align.cc new file mode 100644 index 00000000..9d698074 --- /dev/null +++ b/word-aligner/fast_align.cc @@ -0,0 +1,310 @@ +#include <iostream> +#include <cmath> +#include <utility> +#include <tr1/unordered_map> + +#include <boost/functional/hash.hpp> +#include <boost/program_options.hpp> +#include <boost/program_options/variables_map.hpp> + +#include "m.h" +#include "corpus_tools.h" +#include "stringlib.h" +#include "filelib.h" +#include "ttables.h" +#include "tdict.h" +#include "da.h" + +namespace po = boost::program_options; +using namespace std; +using namespace std::tr1; + +bool InitCommandLine(int argc, char** argv, po::variables_map* conf) { + po::options_description opts("Configuration options"); + opts.add_options() + ("input,i",po::value<string>(),"Parallel corpus input file") + ("reverse,r","Reverse estimation (swap source and target during training)") + ("iterations,I",po::value<unsigned>()->default_value(5),"Number of iterations of EM training") + //("bidir,b", "Run bidirectional alignment") + ("favor_diagonal,d", "Use a static alignment distribution that assigns higher probabilities to alignments near the diagonal") + ("prob_align_null", po::value<double>()->default_value(0.08), "When --favor_diagonal is set, what's the probability of a null alignment?") + ("diagonal_tension,T", po::value<double>()->default_value(4.0), "How sharp or flat around the diagonal is the alignment distribution (<1 = flat >1 = sharp)") + ("optimize_tension,o", "Optimize diagonal tension during EM") + ("variational_bayes,v","Infer VB estimate of parameters under a symmetric Dirichlet prior") + ("alpha,a", po::value<double>()->default_value(0.01), "Hyperparameter for optional Dirichlet prior") + ("no_null_word,N","Do not generate from a null token") + ("output_parameters,p", "Write model parameters instead of alignments") + ("beam_threshold,t",po::value<double>()->default_value(-4),"When writing parameters, log_10 of beam threshold for writing parameter (-10000 to include everything, 0 max parameter only)") + ("hide_training_alignments,H", "Hide training alignments (only useful if you want to use -x option and just compute testset statistics)") + ("testset,x", po::value<string>(), "After training completes, compute the log likelihood of this set of sentence pairs under the learned model") + ("no_add_viterbi,V","When writing model parameters, do not add Viterbi alignment points (may generate a grammar where some training sentence pairs are unreachable)"); + 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 << "Usage " << argv[0] << " [OPTIONS] -i corpus.fr-en\n"; + cerr << dcmdline_options << endl; + return false; + } + return true; +} + +int main(int argc, char** argv) { + po::variables_map conf; + if (!InitCommandLine(argc, argv, &conf)) return 1; + const string fname = conf["input"].as<string>(); + const bool reverse = conf.count("reverse") > 0; + const int ITERATIONS = conf["iterations"].as<unsigned>(); + const double BEAM_THRESHOLD = pow(10.0, conf["beam_threshold"].as<double>()); + const bool use_null = (conf.count("no_null_word") == 0); + const WordID kNULL = TD::Convert("<eps>"); + const bool add_viterbi = (conf.count("no_add_viterbi") == 0); + const bool variational_bayes = (conf.count("variational_bayes") > 0); + const bool write_alignments = (conf.count("output_parameters") == 0); + double diagonal_tension = conf["diagonal_tension"].as<double>(); + bool optimize_tension = conf.count("optimize_tension"); + const bool hide_training_alignments = (conf.count("hide_training_alignments") > 0); + string testset; + if (conf.count("testset")) testset = conf["testset"].as<string>(); + double prob_align_null = conf["prob_align_null"].as<double>(); + double prob_align_not_null = 1.0 - prob_align_null; + const double alpha = conf["alpha"].as<double>(); + const bool favor_diagonal = conf.count("favor_diagonal"); + if (variational_bayes && alpha <= 0.0) { + cerr << "--alpha must be > 0\n"; + return 1; + } + + TTable s2t, t2s; + TTable::Word2Word2Double s2t_viterbi; + unordered_map<pair<short, short>, unsigned, boost::hash<pair<short, short> > > size_counts; + double tot_len_ratio = 0; + double mean_srclen_multiplier = 0; + vector<double> probs; + for (int iter = 0; iter < ITERATIONS; ++iter) { + const bool final_iteration = (iter == (ITERATIONS - 1)); + cerr << "ITERATION " << (iter + 1) << (final_iteration ? " (FINAL)" : "") << endl; + ReadFile rf(fname); + istream& in = *rf.stream(); + double likelihood = 0; + double denom = 0.0; + int lc = 0; + bool flag = false; + string line; + string ssrc, strg; + vector<WordID> src, trg; + double c0 = 0; + double emp_feat = 0; + double toks = 0; + while(true) { + getline(in, line); + if (!in) break; + ++lc; + if (lc % 1000 == 0) { cerr << '.'; flag = true; } + if (lc %50000 == 0) { cerr << " [" << lc << "]\n" << flush; flag = false; } + src.clear(); trg.clear(); + CorpusTools::ReadLine(line, &src, &trg); + if (reverse) swap(src, trg); + if (src.size() == 0 || trg.size() == 0) { + cerr << "Error: " << lc << "\n" << line << endl; + return 1; + } + if (iter == 0) + tot_len_ratio += static_cast<double>(trg.size()) / static_cast<double>(src.size()); + denom += trg.size(); + probs.resize(src.size() + 1); + if (iter == 0) + ++size_counts[make_pair<short,short>(trg.size(), src.size())]; + bool first_al = true; // used for write_alignments + toks += trg.size(); + for (unsigned j = 0; j < trg.size(); ++j) { + const WordID& f_j = trg[j]; + double sum = 0; + double prob_a_i = 1.0 / (src.size() + use_null); // uniform (model 1) + if (use_null) { + if (favor_diagonal) prob_a_i = prob_align_null; + probs[0] = s2t.prob(kNULL, f_j) * prob_a_i; + sum += probs[0]; + } + double az = 0; + if (favor_diagonal) + az = DiagonalAlignment::ComputeZ(j+1, trg.size(), src.size(), diagonal_tension) / prob_align_not_null; + for (unsigned i = 1; i <= src.size(); ++i) { + if (favor_diagonal) + prob_a_i = DiagonalAlignment::UnnormalizedProb(j + 1, i, trg.size(), src.size(), diagonal_tension) / az; + probs[i] = s2t.prob(src[i-1], f_j) * prob_a_i; + sum += probs[i]; + } + if (final_iteration) { + if (add_viterbi || write_alignments) { + WordID max_i = 0; + double max_p = -1; + int max_index = -1; + if (use_null) { + max_i = kNULL; + max_index = 0; + max_p = probs[0]; + } + for (unsigned i = 1; i <= src.size(); ++i) { + if (probs[i] > max_p) { + max_index = i; + max_p = probs[i]; + max_i = src[i-1]; + } + } + if (!hide_training_alignments && write_alignments) { + if (max_index > 0) { + if (first_al) first_al = false; else cout << ' '; + if (reverse) + cout << j << '-' << (max_index - 1); + else + cout << (max_index - 1) << '-' << j; + } + } + s2t_viterbi[max_i][f_j] = 1.0; + } + } else { + if (use_null) { + double count = probs[0] / sum; + c0 += count; + s2t.Increment(kNULL, f_j, count); + } + for (unsigned i = 1; i <= src.size(); ++i) { + const double p = probs[i] / sum; + s2t.Increment(src[i-1], f_j, p); + emp_feat += DiagonalAlignment::Feature(j, i, trg.size(), src.size()) * p; + } + } + likelihood += log(sum); + } + if (write_alignments && final_iteration && !hide_training_alignments) cout << endl; + } + + // log(e) = 1.0 + double base2_likelihood = likelihood / log(2); + + if (flag) { cerr << endl; } + if (iter == 0) { + mean_srclen_multiplier = tot_len_ratio / lc; + cerr << "expected target length = source length * " << mean_srclen_multiplier << endl; + } + emp_feat /= toks; + cerr << " log_e likelihood: " << likelihood << endl; + cerr << " log_2 likelihood: " << base2_likelihood << endl; + cerr << " cross entropy: " << (-base2_likelihood / denom) << endl; + cerr << " perplexity: " << pow(2.0, -base2_likelihood / denom) << endl; + cerr << " posterior p0: " << c0 / toks << endl; + cerr << " posterior al-feat: " << emp_feat << endl; + //cerr << " model tension: " << mod_feat / toks << endl; + cerr << " size counts: " << size_counts.size() << endl; + if (!final_iteration) { + if (favor_diagonal && optimize_tension && iter > 0) { + for (int ii = 0; ii < 8; ++ii) { + double mod_feat = 0; + unordered_map<pair<short,short>,unsigned>::iterator it = size_counts.begin(); + for(; it != size_counts.end(); ++it) { + const pair<short,short>& p = it->first; + for (short j = 1; j <= p.first; ++j) + mod_feat += it->second * DiagonalAlignment::ComputeDLogZ(j, p.first, p.second, diagonal_tension); + } + mod_feat /= toks; + cerr << " " << ii + 1 << " model al-feat: " << mod_feat << " (tension=" << diagonal_tension << ")\n"; + diagonal_tension += (emp_feat - mod_feat) * 20.0; + if (diagonal_tension <= 0.1) diagonal_tension = 0.1; + if (diagonal_tension > 14) diagonal_tension = 14; + } + cerr << " final tension: " << diagonal_tension << endl; + } + if (variational_bayes) + s2t.NormalizeVB(alpha); + else + s2t.Normalize(); + //prob_align_null *= 0.8; // XXX + //prob_align_null += (c0 / toks) * 0.2; + prob_align_not_null = 1.0 - prob_align_null; + } + } + if (testset.size()) { + ReadFile rf(testset); + istream& in = *rf.stream(); + int lc = 0; + double tlp = 0; + string line; + while (getline(in, line)) { + ++lc; + vector<WordID> src, trg; + CorpusTools::ReadLine(line, &src, &trg); + cout << TD::GetString(src) << " ||| " << TD::GetString(trg) << " |||"; + if (reverse) swap(src, trg); + double log_prob = Md::log_poisson(trg.size(), 0.05 + src.size() * mean_srclen_multiplier); + + // compute likelihood + for (unsigned j = 0; j < trg.size(); ++j) { + const WordID& f_j = trg[j]; + double sum = 0; + int a_j = 0; + double max_pat = 0; + double prob_a_i = 1.0 / (src.size() + use_null); // uniform (model 1) + if (use_null) { + if (favor_diagonal) prob_a_i = prob_align_null; + max_pat = s2t.prob(kNULL, f_j) * prob_a_i; + sum += max_pat; + } + double az = 0; + if (favor_diagonal) + az = DiagonalAlignment::ComputeZ(j+1, trg.size(), src.size(), diagonal_tension) / prob_align_not_null; + for (unsigned i = 1; i <= src.size(); ++i) { + if (favor_diagonal) + prob_a_i = DiagonalAlignment::UnnormalizedProb(j + 1, i, trg.size(), src.size(), diagonal_tension) / az; + double pat = s2t.prob(src[i-1], f_j) * prob_a_i; + if (pat > max_pat) { max_pat = pat; a_j = i; } + sum += pat; + } + log_prob += log(sum); + if (write_alignments) { + if (a_j > 0) { + cout << ' '; + if (reverse) + cout << j << '-' << (a_j - 1); + else + cout << (a_j - 1) << '-' << j; + } + } + } + tlp += log_prob; + cout << " ||| " << log_prob << endl << flush; + } // loop over test set sentences + cerr << "TOTAL LOG PROB " << tlp << endl; + } + + if (write_alignments) return 0; + + for (TTable::Word2Word2Double::iterator ei = s2t.ttable.begin(); ei != s2t.ttable.end(); ++ei) { + const TTable::Word2Double& cpd = ei->second; + const TTable::Word2Double& vit = s2t_viterbi[ei->first]; + const string& esym = TD::Convert(ei->first); + double max_p = -1; + for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi) + if (fi->second > max_p) max_p = fi->second; + const double threshold = max_p * BEAM_THRESHOLD; + for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi) { + if (fi->second > threshold || (vit.find(fi->first) != vit.end())) { + cout << esym << ' ' << TD::Convert(fi->first) << ' ' << log(fi->second) << endl; + } + } + } + return 0; +} + |