def align_log_prob(i, j, source_sents, target_sents, alignment, params):
"""Returns the log probability of the two sentences C{source_sents[i]}, C{target_sents[j]}
being aligned with a specific C{alignment}.
@param i: The offset of the source sentence.
@param j: The offset of the target sentence.
@param source_sents: The list of source sentence lengths.
@param target_sents: The list of target sentence lengths.
@param alignment: The alignment type, a tuple of two integers.
@param params: The sentence alignment parameters.
@returns: The log probability of a specific alignment between the two sentences, given the parameters.
"""
l_s = sum(source_sents[i - offset - 1] for offset in range(alignment[0]))
l_t = sum(target_sents[j - offset - 1] for offset in range(alignment[1]))
try:
# actually, the paper says l_s * params.VARIANCE_CHARACTERS, this is based on the C
# reference implementation. With l_s in the denominator, insertions are impossible.
m = (l_s + l_t / params.AVERAGE_CHARACTERS) / 2
delta = (l_s * params.AVERAGE_CHARACTERS - l_t) / math.sqrt(
m * params.VARIANCE_CHARACTERS)
except ZeroDivisionError:
return float('-inf')
return -(LOG2 + norm_logsf(abs(delta)) +
math.log(params.PRIORS[alignment]))
def align_log_prob(i, j, source_sents, target_sents, alignment, params):
"""Returns the log probability of the two sentences C{source_sents[i]}, C{target_sents[j]}
being aligned with a specific C{alignment}.
@param i: The offset of the source sentence.
@param j: The offset of the target sentence.
@param source_sents: The list of source sentence lengths.
@param target_sents: The list of target sentence lengths.
@param alignment: The alignment type, a tuple of two integers.
@param params: The sentence alignment parameters.
@returns: The log probability of a specific alignment between the two sentences, given the parameters.
"""
l_s = sum(source_sents[i - offset - 1] for offset in range(alignment[0]))
l_t = sum(target_sents[j - offset - 1] for offset in range(alignment[1]))
try:
# actually, the paper says l_s * params.VARIANCE_CHARACTERS, this is based on the C
# reference implementation. With l_s in the denominator, insertions are impossible.
m = (l_s + l_t / params.AVERAGE_CHARACTERS) / 2
delta = (l_s * params.AVERAGE_CHARACTERS - l_t) / math.sqrt(m * params.VARIANCE_CHARACTERS)
except ZeroDivisionError:
return float('-inf')
return - (LOG2 + norm_logsf(abs(delta)) + math.log(params.PRIORS[alignment]))