public Dictionary <int, int> GetIntersectionPoints(IBMModel2 model_f_to_e, IBMModel2 model_e_to_f, string[] ws_f, string[] ws_e)
{
Dictionary <int, int> intersection_points;
GetAlignmentMatrix(model_f_to_e, model_e_to_f, ws_f, ws_e, out intersection_points);
return(intersection_points);
}
public static void Initialize(IBMModel2 model, IEnumerable <EMTrainingRecord> training_corpus)
{
foreach (EMTrainingRecord rec in training_corpus)
{
string[] input_lang = rec.InputLang;
string[] output_lang = rec.OutputLang;
int m_input_len = input_lang.Length;
int l_output_len = output_lang.Length;
for (int j = 0; j < m_input_len; ++j)
{
for (int a_j = 0; a_j < l_output_len; ++a_j)
{
string word_input = input_lang[j];
string word_output = output_lang[a_j];
double count_input_output = model.TranslationMap.GetCountOfInputWordAndOutputWord(word_input, word_output);
double count_output = model.TranslationMap.GetCountOfOutputWord(word_output);
model.TranslationMap.SetProbabilityInputWordGivenOutputWord(word_input, word_output, mRandom.NextDouble());
double count_j_given_aj_l_m = model.DistortionMap.GetCountOfAlignmentIndexGivenAlignmentValueAndInputLenAndOutputLen(j, a_j, l_output_len, m_input_len);
double count_aj_l_m = model.DistortionMap.GetCountOfAlignmentValueAndInputLenAndOutputLen(a_j, l_output_len, m_input_len);
model.DistortionMap.SetDistortionParameter(a_j, j, m_input_len, l_output_len, mRandom.NextDouble());
}
}
}
}
public static void Train(IBMModel2 model_f_to_e, IBMModel2 model_e_to_f, IEnumerable <EMTrainingRecord> training_corpus_from_f_to_e, int maxIterations)
{
Train(model_f_to_e, training_corpus_from_f_to_e, maxIterations);
List <EMTrainingRecord> training_corpus_from_e_to_f = new List <EMTrainingRecord>();
foreach (EMTrainingRecord rec in training_corpus_from_f_to_e)
{
EMTrainingRecord rec2 = new EMTrainingRecord()
{
InputLang = rec.OutputLang,
OutputLang = rec.InputLang
};
training_corpus_from_e_to_f.Add(rec2);
}
Train(model_e_to_f, training_corpus_from_f_to_e, maxIterations);
}
public void GetPhrases(IBMModel2 model_f_to_e, IBMModel2 model_e_to_f, string[] ws_f, string[] ws_e, Dictionary <string, string> phrases)
{
Dictionary <int, int> intersection_points = GetIntersectionPoints(model_f_to_e, model_e_to_f, ws_f, ws_e);
List <int> ipList = intersection_points.Keys.ToList();
ipList.Sort();
for (int k = 0; k < ipList.Count - 1; ++k)
{
int j_f1 = ipList[k];
int j_f2 = ipList[k + 1];
int i_e1 = intersection_points[j_f1];
int i_e2 = intersection_points[j_f2];
}
}
public void RunEMTraining()
{
IBMModel2 model = new IBMModel2();
List <EMTrainingRecord> training_corpus = new List <EMTrainingRecord>();
EnglishTokenizer tokenizer_output = new EnglishTokenizer();
FrenchTokenizer tokenizer_input = new FrenchTokenizer();
EMTrainingMethod.Train(model, training_corpus, 20);
string sentence_input = "[Some French Sentence]";
string sentence_output = "[Some English Sentence]";
string[] input_lang = tokenizer_input.Tokenize(sentence_input);
string[] output_lang = tokenizer_output.Tokenize(sentence_output);
int[] alignment = model.GetAlignment(input_lang, output_lang);
Dictionary <int, string> output_mapping = new Dictionary <int, string>();
int m_input_len = input_lang.Length;
for (int j = 0; j < m_input_len; ++j)
{
int a_j = alignment[j];
string output_word = output_lang[a_j];
output_mapping[a_j] = output_word;
}
List <int> output_sentence_index_list = output_mapping.Keys.ToList();
output_sentence_index_list.Sort();
string[] predicted_output_lang = new string[output_sentence_index_list.Count];
for (int i = 0; i < predicted_output_lang.Length; ++i)
{
predicted_output_lang[i] = output_mapping[output_sentence_index_list[i]];
}
Console.WriteLine("Original French Sentence: {0}", sentence_input);
Console.WriteLine("Predicted English Translation: {0}", string.Join(" ", predicted_output_lang));
}
public void GetAlignmentMatrix(IBMModel2 model_f_to_e, IBMModel2 model_e_to_f, string[] ws_f, string[] ws_e, out Dictionary <int, int> intersection_points)
{
int[] alignment_f_to_e = model_f_to_e.GetAlignment(ws_f, ws_e);
int[] alignment_e_to_f = model_e_to_f.GetAlignment(ws_e, ws_f);
int m_f_len = alignment_f_to_e.Length;
int l_e_len = alignment_e_to_f.Length;
intersection_points = new Dictionary <int, int>();
int[][] alignment_matrix = new int[m_f_len][];
for (int j = 0; j < m_f_len; ++j)
{
alignment_matrix[j] = new int[l_e_len];
for (int i = 0; i < l_e_len; ++i)
{
if (alignment_f_to_e[j] == i && alignment_e_to_f[i] == j)
{
intersection_points[j] = i;
alignment_matrix[j][i] = 1;
}
}
}
}
public static void Train(IBMModel2 model, IEnumerable <SimpleTrainingRecord> training_corpus)
{
foreach (SimpleTrainingRecord rec in training_corpus)
{
string[] input_lang = rec.InputLang;
string[] output_lang = rec.OutputLang;
int[] a = rec.Alignment;
int m_input_len = input_lang.Length;
int l_output_len = output_lang.Length;
Debug.Assert(m_input_len == a.Length);
for (int j = 0; j < m_input_len; ++j)
{
int a_j = a[j];
string word_input = input_lang[j];
string word_output = output_lang[a_j];
double count_input_output = model.TranslationMap.GetCountOfInputWordAndOutputWord(word_input, word_output);
double count_output = model.TranslationMap.GetCountOfOutputWord(word_output);
model.TranslationMap.SetCountOfInputWordAndOutputWord(word_input, word_output, count_input_output + 1);
model.TranslationMap.SetCountOfOutputWord(word_output, count_output + 1);
model.TranslationMap.UpdateProbabilityInputWordGivenOutputWord(word_input, word_output);
double count_j_given_aj_l_m = model.DistortionMap.GetCountOfAlignmentIndexGivenAlignmentValueAndInputLenAndOutputLen(j, a_j, l_output_len, m_input_len);
double count_aj_l_m = model.DistortionMap.GetCountOfAlignmentValueAndInputLenAndOutputLen(a_j, l_output_len, m_input_len);
model.DistortionMap.SetCountOfAlignmentIndexGivenAlignmentValueAndInputLenAndOutputLen(j, a_j, l_output_len, m_input_len, count_j_given_aj_l_m + 1);
model.DistortionMap.SetCountOfAlignmentValueAndInputLenAndOutputLen(a_j, l_output_len, m_input_len, count_aj_l_m + 1);
model.DistortionMap.UpdateDistortionParameter(a_j, j, m_input_len, l_output_len);
}
}
}
public static void Train(IBMModel2 model, IEnumerable <EMTrainingRecord> training_corpus, int maxIterations)
{
for (int iteration = 0; iteration < maxIterations; ++iteration)
{
foreach (EMTrainingRecord rec in training_corpus)
{
string[] input_lang = rec.InputLang;
string[] output_lang = rec.OutputLang;
int m_input_len = input_lang.Length;
int l_output_len = output_lang.Length;
double[][] delta = new double[m_input_len][];
double sum = 0;
for (int j = 0; j < m_input_len; ++j)
{
delta[j] = new double[l_output_len];
string word_input = input_lang[j];
for (int a_j = 0; a_j < l_output_len; ++a_j)
{
string word_output = output_lang[a_j];
double t = model.TranslationMap.GetProbabilityInputWordGivenOutputWord(word_input, word_output);
double q = model.DistortionMap.GetDistortionParameter(a_j, j, m_input_len, l_output_len);
double deltaVal = t * q;
delta[j][a_j] = deltaVal;
sum += deltaVal;
}
}
for (int j = 0; j < m_input_len; ++j)
{
for (int a_j = 0; a_j < l_output_len; ++a_j)
{
delta[j][a_j] /= sum;
}
}
for (int j = 0; j < m_input_len; ++j)
{
for (int a_j = 0; a_j < l_output_len; ++a_j)
{
double deltaVal = delta[j][a_j];
string word_input = input_lang[j];
string word_output = output_lang[a_j];
double count_input_output = model.TranslationMap.GetCountOfInputWordAndOutputWord(word_input, word_output);
double count_output = model.TranslationMap.GetCountOfOutputWord(word_output);
model.TranslationMap.SetCountOfInputWordAndOutputWord(word_input, word_output, count_input_output + deltaVal);
model.TranslationMap.SetCountOfOutputWord(word_output, count_output + deltaVal);
model.TranslationMap.UpdateProbabilityInputWordGivenOutputWord(word_input, word_output);
double count_j_given_aj_l_m = model.DistortionMap.GetCountOfAlignmentIndexGivenAlignmentValueAndInputLenAndOutputLen(j, a_j, l_output_len, m_input_len);
double count_aj_l_m = model.DistortionMap.GetCountOfAlignmentValueAndInputLenAndOutputLen(a_j, l_output_len, m_input_len);
model.DistortionMap.SetCountOfAlignmentIndexGivenAlignmentValueAndInputLenAndOutputLen(j, a_j, l_output_len, m_input_len, count_j_given_aj_l_m + deltaVal);
model.DistortionMap.SetCountOfAlignmentValueAndInputLenAndOutputLen(a_j, l_output_len, m_input_len, count_aj_l_m + deltaVal);
model.DistortionMap.UpdateDistortionParameter(a_j, j, m_input_len, l_output_len);
}
}
}
}
}
public void InitializeIBMModels(IBMModel2 model_f_to_e, IBMModel2 model_e_to_f, IEnumerable <EMTrainingRecord> training_corpus_f_to_e, int maxIterations)
{
EMTrainingMethod.Train(model_f_to_e, model_e_to_f, training_corpus_f_to_e, maxIterations);
}
