Classical Machine Translation algorithms such as IBM models and EM methods in .NET
Install-Package cs-nlp-classical-machine-translationThe sample codes below shows how to train and do language translation with IBM model 1:
using System;
using System.Collections.Generic;
using System.Linq;
namespace ClassicalMachineTranslation
{
class IBMModel1Demo
{
public void RunSimpleTraining()
{
IBMModel1 model = new IBMModel1();
List<SimpleTrainingRecord> training_corpus = new List<SimpleTrainingRecord>();
EnglishTokenizer tokenizer_output = new EnglishTokenizer();
FrenchTokenizer tokenizer_input = new FrenchTokenizer();
SimpleTrainingMethod.Train(model, training_corpus);
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 RunEMTraining()
{
IBMModel1 model = new IBMModel1();
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));
}
}
}The sample codes below show how to train and do language translation using IBM model 2:
uusing System;
using System.Collections.Generic;
using System.Linq;
namespace ClassicalMachineTranslation
{
class IBMModel2Demo
{
public void RunSimpleTraining()
{
IBMModel2 model = new IBMModel2();
List<SimpleTrainingRecord> training_corpus = new List<SimpleTrainingRecord>();
EnglishTokenizer tokenizer_output = new EnglishTokenizer();
FrenchTokenizer tokenizer_input = new FrenchTokenizer();
SimpleTrainingMethod.Train(model, training_corpus);
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 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));
}
}
}