def test_best_model2_alignment_handles_fertile_words(self):
# arrange
sentence_pair = AlignedSent(
['i', 'really', ',', 'really', 'love', 'ham'],
TestIBMModel.__TEST_SRC_SENTENCE)
# 'bien' produces 2 target words: 'really' and another 'really'
translation_table = {
'i': {"j'": 0.9, 'aime': 0.05, 'bien': 0.02, 'jambon': 0.03, None: 0},
'really': {"j'": 0, 'aime': 0, 'bien': 0.9, 'jambon': 0.01, None: 0.09},
',': {"j'": 0, 'aime': 0, 'bien': 0.3, 'jambon': 0, None: 0.7},
'love': {"j'": 0.05, 'aime': 0.9, 'bien': 0.01, 'jambon': 0.01, None: 0.03},
'ham': {"j'": 0, 'aime': 0.01, 'bien': 0, 'jambon': 0.99, None: 0}
}
alignment_table = defaultdict(
lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(
lambda: 0.2))))
ibm_model = IBMModel([])
ibm_model.translation_table = translation_table
ibm_model.alignment_table = alignment_table
# act
a_info = ibm_model.best_model2_alignment(sentence_pair)
# assert
self.assertEqual(a_info.alignment[1:], (1, 3, 0, 3, 2, 4))
self.assertEqual(a_info.cepts, [[3], [1], [5], [2, 4], [6]])
def test_best_model2_alignment_does_not_change_pegged_alignment(self):
# arrange
sentence_pair = AlignedSent(
TestIBMModel.__TEST_TRG_SENTENCE,
TestIBMModel.__TEST_SRC_SENTENCE)
translation_table = {
'i': {"j'": 0.9, 'aime': 0.05, 'bien': 0.02, 'jambon': 0.03,
None: 0},
'love': {"j'": 0.05, 'aime': 0.9, 'bien': 0.01, 'jambon': 0.01,
None: 0.03},
'ham': {"j'": 0, 'aime': 0.01, 'bien': 0, 'jambon': 0.99, None: 0}
}
alignment_table = defaultdict(
lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(
lambda: 0.2))))
ibm_model = IBMModel([])
ibm_model.translation_table = translation_table
ibm_model.alignment_table = alignment_table
# act: force 'love' to be pegged to 'jambon'
a_info = ibm_model.best_model2_alignment(sentence_pair, 2, 4)
# assert
self.assertEqual(a_info.alignment[1:], (1, 4, 4))
self.assertEqual(a_info.cepts, [[], [1], [], [], [2, 3]])
def test_best_model2_alignment(self):
# arrange
sentence_pair = AlignedSent(
TestIBMModel.__TEST_TRG_SENTENCE,
TestIBMModel.__TEST_SRC_SENTENCE)
# None and 'bien' have zero fertility
translation_table = {
'i': {"j'": 0.9, 'aime': 0.05, 'bien': 0.02, 'jambon': 0.03,
None: 0},
'love': {"j'": 0.05, 'aime': 0.9, 'bien': 0.01, 'jambon': 0.01,
None: 0.03},
'ham': {"j'": 0, 'aime': 0.01, 'bien': 0, 'jambon': 0.99,
None: 0}
}
alignment_table = defaultdict(
lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(
lambda: 0.2))))
ibm_model = IBMModel([])
ibm_model.translation_table = translation_table
ibm_model.alignment_table = alignment_table
# act
a_info = ibm_model.best_model2_alignment(sentence_pair)
# assert
self.assertEqual(a_info.alignment[1:], (1, 2, 4)) # 0th element unused
self.assertEqual(a_info.cepts, [[], [1], [2], [], [3]])
def test_best_model2_alignment_handles_fertile_words(self):
# arrange
sentence_pair = AlignedSent(
['i', 'really', ',', 'really', 'love', 'ham'],
TestIBMModel.__TEST_SRC_SENTENCE)
# 'bien' produces 2 target words: 'really' and another 'really'
translation_table = {
'i': {
"j'": 0.9,
'aime': 0.05,
'bien': 0.02,
'jambon': 0.03,
None: 0
},
'really': {
"j'": 0,
'aime': 0,
'bien': 0.9,
'jambon': 0.01,
None: 0.09
},
',': {
"j'": 0,
'aime': 0,
'bien': 0.3,
'jambon': 0,
None: 0.7
},
'love': {
"j'": 0.05,
'aime': 0.9,
'bien': 0.01,
'jambon': 0.01,
None: 0.03
},
'ham': {
"j'": 0,
'aime': 0.01,
'bien': 0,
'jambon': 0.99,
None: 0
}
}
alignment_table = defaultdict(lambda: defaultdict(lambda: defaultdict(
lambda: defaultdict(lambda: 0.2))))
ibm_model = IBMModel([])
ibm_model.translation_table = translation_table
ibm_model.alignment_table = alignment_table
# act
a_info = ibm_model.best_model2_alignment(sentence_pair)
# assert
self.assertEqual(a_info.alignment[1:], (1, 3, 0, 3, 2, 4))
self.assertEqual(a_info.cepts, [[3], [1], [5], [2, 4], [6]])
def test_best_model2_alignment(self):
# arrange
sentence_pair = AlignedSent(TestIBMModel.__TEST_TRG_SENTENCE,
TestIBMModel.__TEST_SRC_SENTENCE)
# None and 'bien' have zero fertility
translation_table = {
'i': {
"j'": 0.9,
'aime': 0.05,
'bien': 0.02,
'jambon': 0.03,
None: 0
},
'love': {
"j'": 0.05,
'aime': 0.9,
'bien': 0.01,
'jambon': 0.01,
None: 0.03
},
'ham': {
"j'": 0,
'aime': 0.01,
'bien': 0,
'jambon': 0.99,
None: 0
}
}
alignment_table = defaultdict(lambda: defaultdict(lambda: defaultdict(
lambda: defaultdict(lambda: 0.2))))
ibm_model = IBMModel([])
ibm_model.translation_table = translation_table
ibm_model.alignment_table = alignment_table
# act
a_info = ibm_model.best_model2_alignment(sentence_pair)
# assert
self.assertEqual(a_info.alignment[1:], (1, 2, 4)) # 0th element unused
self.assertEqual(a_info.cepts, [[], [1], [2], [], [3]])
def test_best_model2_alignment_does_not_change_pegged_alignment(self):
# arrange
sentence_pair = AlignedSent(TestIBMModel.__TEST_TRG_SENTENCE,
TestIBMModel.__TEST_SRC_SENTENCE)
translation_table = {
'i': {
"j'": 0.9,
'aime': 0.05,
'bien': 0.02,
'jambon': 0.03,
None: 0
},
'love': {
"j'": 0.05,
'aime': 0.9,
'bien': 0.01,
'jambon': 0.01,
None: 0.03
},
'ham': {
"j'": 0,
'aime': 0.01,
'bien': 0,
'jambon': 0.99,
None: 0
}
}
alignment_table = defaultdict(lambda: defaultdict(lambda: defaultdict(
lambda: defaultdict(lambda: 0.2))))
ibm_model = IBMModel([])
ibm_model.translation_table = translation_table
ibm_model.alignment_table = alignment_table
# act: force 'love' to be pegged to 'jambon'
a_info = ibm_model.best_model2_alignment(sentence_pair, 2, 4)
# assert
self.assertEqual(a_info.alignment[1:], (1, 4, 4))
self.assertEqual(a_info.cepts, [[], [1], [], [], [2, 3]])