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authorChris Dyer <cdyer@cs.cmu.edu>2012-01-20 15:35:47 -0500
committerChris Dyer <cdyer@cs.cmu.edu>2012-01-20 15:35:47 -0500
commitf0bdd4de6455855d705d9056deb2e90c999dc740 (patch)
treeac4ebe783d4f613c6f0b01a28210b014f7ba7ed6 /training
parentd156a65ac638b574abfabfd78c949e122faada5d (diff)
'pseudo model 2' that strictly favors a diagonal, with tunable parameters for p(null) and how sharp/flat the alignment distribution is around the diagonal
Diffstat (limited to 'training')
-rw-r--r--training/model1.cc39
1 files changed, 36 insertions, 3 deletions
diff --git a/training/model1.cc b/training/model1.cc
index b9590ece..346c0033 100644
--- a/training/model1.cc
+++ b/training/model1.cc
@@ -20,6 +20,10 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
("iterations,i",po::value<unsigned>()->default_value(5),"Number of iterations of EM training")
("beam_threshold,t",po::value<double>()->default_value(-4),"log_10 of beam threshold (-10000 to include everything, 0 max)")
("no_null_word,N","Do not generate from the null token")
+ ("write_alignments,A", "Write alignments instead of parameters")
+ ("favor_diagonal,d", "Use a static alignment distribution that assigns higher probabilities to alignments near the diagonal")
+ ("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)")
+ ("prob_align_null", po::value<double>()->default_value(0.08), "When --favor_diagonal is set, what's the probability of a null alignment?")
("variational_bayes,v","Add a symmetric Dirichlet prior and infer VB estimate of weights")
("alpha,a", po::value<double>()->default_value(0.01), "Hyperparameter for optional Dirichlet prior")
("no_add_viterbi,V","Do not add Viterbi alignment points (may generate a grammar where some training sentence pairs are unreachable)");
@@ -56,7 +60,12 @@ int main(int argc, char** argv) {
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("write_alignments") > 0);
+ const double diagonal_tension = conf["diagonal_tension"].as<double>();
+ const double prob_align_null = conf["prob_align_null"].as<double>();
+ const 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;
@@ -93,31 +102,52 @@ int main(int argc, char** argv) {
denom += trg.size();
vector<double> probs(src.size() + 1);
const double src_logprob = -log(src.size() + 1);
+ bool first_al = true; // used for write_alignments
for (int j = 0; j < trg.size(); ++j) {
const WordID& f_j = trg[j][0].label;
double sum = 0;
+ const double j_over_ts = double(j) / trg.size();
+ double prob_a_i = 1.0 / (src.size() + use_null); // uniform (model 1)
if (use_null) {
- probs[0] = tt.prob(kNULL, f_j);
+ if (favor_diagonal) prob_a_i = prob_align_null;
+ probs[0] = tt.prob(kNULL, f_j) * prob_a_i;
sum += probs[0];
}
+ double az = 0;
+ if (favor_diagonal) {
+ for (int ta = 0; ta < src.size(); ++ta)
+ az += exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension);
+ az /= prob_align_not_null;
+ }
for (int i = 1; i <= src.size(); ++i) {
- probs[i] = tt.prob(src[i-1][0].label, f_j);
+ if (favor_diagonal)
+ prob_a_i = exp(-fabs(double(i) / src.size() - j_over_ts) * diagonal_tension) / az;
+ probs[i] = tt.prob(src[i-1][0].label, f_j) * prob_a_i;
sum += probs[i];
}
if (final_iteration) {
- if (add_viterbi) {
+ 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 (int i = 1; i <= src.size(); ++i) {
if (probs[i] > max_p) {
+ max_index = i;
max_p = probs[i];
max_i = src[i-1][0].label;
}
}
+ if (write_alignments) {
+ if (max_index > 0) {
+ if (first_al) first_al = false; else cout << ' ';
+ cout << (max_index - 1) << "-" << j;
+ }
+ }
was_viterbi[max_i][f_j] = 1.0;
}
} else {
@@ -128,6 +158,7 @@ int main(int argc, char** argv) {
}
likelihood += log(sum) + src_logprob;
}
+ if (write_alignments && final_iteration) cout << endl;
}
// log(e) = 1.0
@@ -145,6 +176,8 @@ int main(int argc, char** argv) {
tt.Normalize();
}
}
+ if (write_alignments) return 0;
+
for (TTable::Word2Word2Double::iterator ei = tt.ttable.begin(); ei != tt.ttable.end(); ++ei) {
const TTable::Word2Double& cpd = ei->second;
const TTable::Word2Double& vit = was_viterbi[ei->first];