public Alignment align(SentencePair sentencePair) {
// Placeholder code below.
// TODO Implement an inference algorithm for Eq.1 in the assignment
// handout to predict alignments based on the counts you collected with train().
Alignment alignment = new Alignment();
List<String> sourceSentence = sentencePair.getSourceWords();
List<String> targetSentence = sentencePair.getTargetWords();
int m = sourceSentence.size();
int l = targetSentence.size();
Pair<Integer, Integer> lmPair = new Pair<Integer, Integer>(l, m);
for (int i = 0; i < m; i++) {
String sourceWord = sourceSentence.get(i);
double bestScore = Double.NEGATIVE_INFINITY;
int bestAlign = -1;
for (int j = 0; j < l; j++) {
String targetWord = targetSentence.get(j);
Pair<Integer, Integer> jiPair = new Pair<Integer, Integer>(j, i);
double transProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double alignProb = sourceTargetDistortions.getCount(jiPair, lmPair);
double score = Math.log(transProb) + Math.log(alignProb);
if (score > bestScore) {
bestScore = score;
bestAlign = j;
}
}
if (!targetSentence.get(bestAlign).equals(WordAligner.NULL_WORD)) {
alignment.addPredictedAlignment(bestAlign, i);
}
}
return alignment;
}
public void train(List<SentencePair> trainingPairs) {
sourceTargetCounts = new CounterMap<String, String>();
sourceTargetDistortions = new CounterMap<Pair<Integer, Integer>, Pair<Integer, Integer>>();
for (SentencePair pair : trainingPairs) {
List<String> sourceSentence = pair.getSourceWords();
List<String> targetSentence = pair.getTargetWords();
targetSentence.add(WordAligner.NULL_WORD);
int m = sourceSentence.size();
int l = targetSentence.size();
for (int i = 0; i < m; i++) {
String sourceWord = sourceSentence.get(i);
for (int j = 0; j < l; j++) {
String targetWord = targetSentence.get(j);
sourceTargetCounts.setCount(sourceWord, targetWord, 1.0);
Pair<Integer, Integer> lmPair = new Pair<Integer, Integer>(l, m);
Pair<Integer, Integer> jiPair = new Pair<Integer, Integer>(j, i);
sourceTargetDistortions.setCount(jiPair, lmPair, 1.0);
}
}
}
// Use Model 1 to train params
double delta = Double.POSITIVE_INFINITY;
for (int i = 0; i < MAX_ITERS && delta > CONVERGENCE; i++) {
CounterMap<String, String> tempSourceTargetCounts = new CounterMap<String, String>();
Counter<String> targetCounts = new Counter<String>();
delta = 0.0;
for (SentencePair pair : trainingPairs) {
List<String> sourceSentence = pair.getSourceWords();
List<String> targetSentence = pair.getTargetWords();
Counter<String> sourceTotals = new Counter<String>();
for (String sourceWord : sourceSentence) {
for (String targetWord : targetSentence) {
sourceTotals.incrementCount(
sourceWord, sourceTargetCounts.getCount(sourceWord, targetWord));
}
}
for (String sourceWord : sourceSentence) {
for (String targetWord : targetSentence) {
double transProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double sourceTotal = sourceTotals.getCount(sourceWord);
tempSourceTargetCounts.incrementCount(sourceWord, targetWord, transProb / sourceTotal);
targetCounts.incrementCount(targetWord, transProb / sourceTotal);
}
}
}
// update t(s|t) values
for (String sourceWord : tempSourceTargetCounts.keySet()) {
for (String targetWord : tempSourceTargetCounts.getCounter(sourceWord).keySet()) {
double oldProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double newProb =
tempSourceTargetCounts.getCount(sourceWord, targetWord)
/ targetCounts.getCount(targetWord);
sourceTargetCounts.setCount(sourceWord, targetWord, newProb);
delta += Math.pow(oldProb - newProb, 2.0);
}
}
delta /= sourceTargetCounts.totalSize();
}
// Maximizing for ibm model 2
delta = Double.POSITIVE_INFINITY;
for (int iter = 0; iter < MAX_ITERS && delta > CONVERGENCE; iter++) {
CounterMap<String, String> tempSourceTargetCounts = new CounterMap<String, String>();
CounterMap<Pair<Integer, Integer>, Pair<Integer, Integer>> tempSourceTargetDistortions =
new CounterMap<Pair<Integer, Integer>, Pair<Integer, Integer>>();
Counter<String> targetCounts = new Counter<String>();
CounterMap<Pair<Integer, Integer>, Integer> targetDistorts =
new CounterMap<Pair<Integer, Integer>, Integer>();
delta = 0.0;
for (SentencePair pair : trainingPairs) {
List<String> sourceSentence = pair.getSourceWords();
List<String> targetSentence = pair.getTargetWords();
CounterMap<Pair<Integer, Integer>, Integer> distortSourceTotals =
new CounterMap<Pair<Integer, Integer>, Integer>();
Pair<Integer, Integer> lmPair =
new Pair<Integer, Integer>(targetSentence.size(), sourceSentence.size());
for (int i = 0; i < sourceSentence.size(); i++) {
String sourceWord = sourceSentence.get(i);
for (int j = 0; j < targetSentence.size(); j++) {
String targetWord = targetSentence.get(j);
Pair<Integer, Integer> jiPair = new Pair<Integer, Integer>(j, i);
double currTransProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double currAlignProb = sourceTargetDistortions.getCount(jiPair, lmPair);
distortSourceTotals.incrementCount(lmPair, i, currTransProb * currAlignProb);
}
}
for (int i = 0; i < sourceSentence.size(); i++) {
String sourceWord = sourceSentence.get(i);
double distortTransSourceTotal = distortSourceTotals.getCount(lmPair, i);
for (int j = 0; j < targetSentence.size(); j++) {
String targetWord = targetSentence.get(j);
Pair<Integer, Integer> jiPair = new Pair<Integer, Integer>(j, i);
double transProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double distortProb = sourceTargetDistortions.getCount(jiPair, lmPair);
double update =
(transProb * distortProb) / (distortTransSourceTotal); // q(j|ilm)t(f|e)/totals
tempSourceTargetCounts.incrementCount(sourceWord, targetWord, update);
tempSourceTargetDistortions.incrementCount(jiPair, lmPair, update);
targetCounts.incrementCount(targetWord, update);
targetDistorts.incrementCount(lmPair, i, update);
}
}
}
// update t(s|t) values
double delta_trans = 0.0;
for (String sourceWord : tempSourceTargetCounts.keySet()) {
for (String targetWord : tempSourceTargetCounts.getCounter(sourceWord).keySet()) {
double oldProb = sourceTargetCounts.getCount(sourceWord, targetWord);
double newProb =
tempSourceTargetCounts.getCount(sourceWord, targetWord)
/ targetCounts.getCount(targetWord);
sourceTargetCounts.setCount(sourceWord, targetWord, newProb);
delta += Math.pow(oldProb - newProb, 2.0);
}
}
// update q(j|ilm) values
double delta_dist = 0.0;
for (Pair<Integer, Integer> jiPair : tempSourceTargetDistortions.keySet()) {
for (Pair<Integer, Integer> lmPair :
tempSourceTargetDistortions.getCounter(jiPair).keySet()) {
double oldProb = sourceTargetDistortions.getCount(jiPair, lmPair);
double tempAlignProb = tempSourceTargetDistortions.getCount(jiPair, lmPair);
double tempTargetDist = targetDistorts.getCount(lmPair, jiPair.getSecond());
double newProb = tempAlignProb / tempTargetDist;
sourceTargetDistortions.setCount(jiPair, lmPair, newProb);
delta_dist += Math.pow(oldProb - newProb, 2.0);
}
}
delta =
(delta_trans / sourceTargetCounts.totalSize()
+ delta_dist / sourceTargetDistortions.totalSize())
/ 2.0;
}
}