private void TuneTranslationModel(string tuneTMPrefix, string tuneLMPrefix,
IReadOnlyList <IReadOnlyList <string> > tuneSourceCorpus,
IReadOnlyList <IReadOnlyList <string> > tuneTargetCorpus, ThotTrainProgressReporter reporter)
{
reporter.Step("Tuning translation model");
if (tuneSourceCorpus.Count == 0)
{
return;
}
string phraseTableFileName = tuneTMPrefix + ".ttable";
FilterPhraseTableUsingCorpus(phraseTableFileName, tuneSourceCorpus);
FilterPhraseTableNBest(phraseTableFileName, 20);
ThotSmtParameters oldParameters = Parameters;
ThotSmtParameters initialParameters = oldParameters.Clone();
initialParameters.TranslationModelFileNamePrefix = tuneTMPrefix;
initialParameters.LanguageModelFileNamePrefix = tuneLMPrefix;
initialParameters.ModelWeights = new[] { 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 0f };
initialParameters.Freeze();
ThotSmtParameters tunedParameters = _modelWeightTuner.Tune(initialParameters, tuneSourceCorpus,
tuneTargetCorpus, reporter, Stats);
Parameters = tunedParameters.Clone();
Parameters.TranslationModelFileNamePrefix = oldParameters.TranslationModelFileNamePrefix;
Parameters.LanguageModelFileNamePrefix = oldParameters.LanguageModelFileNamePrefix;
Parameters.Freeze();
}
public ThotSmtParameters Tune(ThotSmtParameters parameters,
IReadOnlyList <IReadOnlyList <string> > tuneSourceCorpus,
IReadOnlyList <IReadOnlyList <string> > tuneTargetCorpus, ThotTrainProgressReporter reporter,
SmtBatchTrainStats stats)
{
float sentLenWeight = parameters.ModelWeights[7];
int numFuncEvals = 0;
double Evaluate(Vector weights)
{
ThotSmtParameters newParameters = parameters.Clone();
newParameters.ModelWeights = weights.Select(w => (float)w).Concat(sentLenWeight).ToArray();
newParameters.Freeze();
double quality = CalculateBleu(newParameters, tuneSourceCorpus, tuneTargetCorpus);
numFuncEvals++;
if (numFuncEvals < MaxFunctionEvaluations && ProgressIncrementInterval > 0 &&
numFuncEvals % ProgressIncrementInterval == 0)
{
reporter.Step();
}
else
{
reporter.CheckCanceled();
}
return(quality);
};
var simplex = new NelderMeadSimplex(ConvergenceTolerance, MaxFunctionEvaluations, 1.0);
MinimizationResult result = simplex.FindMinimum(Evaluate,
parameters.ModelWeights.Select(w => (double)w).Take(7));
stats.TranslationModelBleu = 1.0 - result.ErrorValue;
ThotSmtParameters bestParameters = parameters.Clone();
bestParameters.ModelWeights = result.MinimizingPoint.Select(w => (float)w).Concat(sentLenWeight).ToArray();
bestParameters.Freeze();
return(bestParameters);
}
public ThotSmtParameters Tune(ThotSmtParameters parameters,
IReadOnlyList <IReadOnlyList <string> > tuneSourceCorpus,
IReadOnlyList <IReadOnlyList <string> > tuneTargetCorpus, SmtBatchTrainStats stats,
IProgress <ProgressStatus> progress)
{
float sentLenWeight = parameters.ModelWeights[7];
int numFuncEvals = 0;
double Evaluate(Vector weights)
{
ThotSmtParameters newParameters = parameters.Clone();
newParameters.ModelWeights = weights.Select(w => (float)w).Concat(sentLenWeight).ToArray();
newParameters.Freeze();
double quality = CalculateBleu(newParameters, tuneSourceCorpus, tuneTargetCorpus);
numFuncEvals++;
int currentStep = Math.Min(numFuncEvals, MaxProgressFunctionEvaluations);
progress.Report(new ProgressStatus(currentStep, MaxProgressFunctionEvaluations));
return(quality);
};
progress.Report(new ProgressStatus(0, MaxFunctionEvaluations));
var simplex = new NelderMeadSimplex(ConvergenceTolerance, MaxFunctionEvaluations, 1.0);
MinimizationResult result = simplex.FindMinimum(Evaluate,
parameters.ModelWeights.Select(w => (double)w).Take(7));
stats.TranslationModelBleu = 1.0 - result.ErrorValue;
ThotSmtParameters bestParameters = parameters.Clone();
bestParameters.ModelWeights = result.MinimizingPoint.Select(w => (float)w).Concat(sentLenWeight).ToArray();
bestParameters.Freeze();
if (result.FunctionEvaluationCount < MaxProgressFunctionEvaluations)
{
progress.Report(new ProgressStatus(1.0));
}
return(bestParameters);
}
public ThotSmtParameters Tune(ThotSmtParameters parameters,
IReadOnlyList <IReadOnlyList <string> > tuneSourceCorpus,
IReadOnlyList <IReadOnlyList <string> > tuneTargetCorpus, SmtBatchTrainStats stats,
IProgress <ProgressStatus> progress)
{
IntPtr weightUpdaterHandle = Thot.llWeightUpdater_create();
try
{
var iterQualities = new List <double>();
double bestQuality = double.MinValue;
ThotSmtParameters bestParameters = null;
int iter = 0;
HashSet <TranslationInfo>[] curNBestLists = null;
float[] curWeights = parameters.ModelWeights.ToArray();
while (true)
{
progress.Report(new ProgressStatus(iter, MaxIterations));
ThotSmtParameters newParameters = parameters.Clone();
newParameters.ModelWeights = curWeights;
newParameters.Freeze();
IList <TranslationInfo>[] nbestLists = GetNBestLists(newParameters, tuneSourceCorpus).ToArray();
double quality = Evaluation.CalculateBleu(nbestLists.Select(nbl => nbl.First().Translation),
tuneTargetCorpus);
iterQualities.Add(quality);
if (quality > bestQuality)
{
bestQuality = quality;
bestParameters = newParameters;
}
iter++;
if (iter >= MaxIterations || IsTuningConverged(iterQualities))
{
break;
}
if (curNBestLists == null)
{
curNBestLists = nbestLists.Select(nbl => new HashSet <TranslationInfo>(nbl)).ToArray();
}
else
{
for (int i = 0; i < nbestLists.Length; i++)
{
curNBestLists[i].UnionWith(nbestLists[i]);
}
}
UpdateWeights(weightUpdaterHandle, tuneTargetCorpus, curNBestLists, curWeights);
}
if (iter < MaxIterations)
{
progress.Report(new ProgressStatus(1.0));
}
stats.TranslationModelBleu = bestQuality;
return(bestParameters);
}
finally
{
Thot.llWeightUpdater_close(weightUpdaterHandle);
}
}
