def prepare_masked_instances_for_humans(sentences, config):
masked_examples = {}
for truism in sentences:
for perturbation in sentences[truism]:
if 'paraphrase' not in perturbation:
candidate_answers = config[truism]['premise_switch']['0']
elif '_inversion' not in perturbation:
candidate_answers = config[truism]['premise_switch']['1']
else:
candidate_answers = config[truism]['premise_switch']['2']
for premise in sentences[truism][perturbation]:
key = "-".join([truism, perturbation, premise])
statement = sentences[truism][perturbation][premise]
premise = statement.split(",")[0]
conclusion = statement.split(",")[1]
right_answer = None
wrong_answer = None
for answer in candidate_answers:
if pad_string(answer, False) in conclusion:
conclusion = conclusion.replace(
" " + answer + " ", " _____ ")
right_answer = answer
else:
wrong_answer = answer
if right_answer and wrong_answer:
masked_statement = premise + ", " + conclusion
masked_examples[key] = (masked_statement, right_answer,
wrong_answer)
return masked_examples
def prep_ft_instances_for_sampling_by_sets(sentences, config,
fictitious_entities,
num_entity_trials):
random.seed(1012)
statements = []
for truism in sentences:
for perturbation in sentences[truism]:
if 'paraphrase' not in perturbation:
candidate_answers = config[truism]['premise_switch']['0']
elif '_inversion' not in perturbation:
candidate_answers = config[truism]['premise_switch']['1']
else:
candidate_answers = config[truism]['premise_switch']['2']
for premise in sentences[truism][perturbation]:
statement = sentences[truism][perturbation][premise]
premise = statement.split(",")[0]
conclusion = statement.split(",")[1]
right_answer = None
for answer in candidate_answers:
if pad_string(answer, False) in conclusion:
right_answer = answer
for entity_pair in random.sample(fictitious_entities,
num_entity_trials):
new_statement = re.sub(r"\bA\b", entity_pair[0], statement)
new_statement = re.sub(r"\bB\b", entity_pair[1],
new_statement)
statements.append((new_statement, right_answer))
return statements
def prepare_masked_instances(sentences, config, fictitious_entities,
num_entity_trials):
masked_examples = {}
for truism in sentences:
for perturbation in sentences[truism]:
if config != None:
if 'paraphrase' not in perturbation:
candidate_answers = config[truism]['premise_switch']['0']
elif '_inversion' not in perturbation:
candidate_answers = config[truism]['premise_switch']['1']
else:
candidate_answers = config[truism]['premise_switch']['2']
for premise in sentences[truism][perturbation]:
key = "-".join([truism, perturbation, premise])
if config == None:
if sentences[truism][perturbation][premise] == []:
continue
candidate_answers = [
sentences[truism][perturbation][premise][2],
sentences[truism][perturbation][premise][3]
]
statement = sentences[truism][perturbation][premise][0]
else:
statement = sentences[truism][perturbation][premise]
premise = statement.split(",")[0]
conclusion = statement.split(",")[1]
right_answer = None
wrong_answer = None
for answer in candidate_answers:
if pad_string(answer, False) in conclusion:
conclusion = conclusion.replace(
" " + answer + " ", " <mask> ")
right_answer = answer
else:
wrong_answer = answer
if right_answer and wrong_answer:
masked_statement = premise + "," + conclusion
masked_examples[key] = []
for entity_pair in random.sample(fictitious_entities,
num_entity_trials):
new_masked_statement = re.sub(r"\bA\b", entity_pair[0],
masked_statement)
new_masked_statement = re.sub(
r"\bB\b", entity_pair[1],
new_masked_statement).capitalize()
masked_examples[key].append(
(new_masked_statement, right_answer, wrong_answer))
return masked_examples
def prepare_masked_easy_instances(sentences, config, fictitious_entities,
num_entity_trials):
masked_examples = {}
for truism in sentences:
for perturbation in sentences[truism]:
candidate_answers = config[truism]['premise_switch']['0']
for premise in sentences[truism][perturbation]:
key = "-".join([truism, perturbation, premise])
statement = sentences[truism][perturbation][premise]
parts = statement.split(",")
masked_portion = parts[len(parts) - 1]
right_answer = None
wrong_answer = None
for answer in candidate_answers:
if pad_string(answer, False) in masked_portion:
masked_portion = masked_portion.replace(
" " + answer + " ", " <mask> ")
right_answer = answer
else:
wrong_answer = answer
masked_statement = ""
for i in range(len(parts) - 1):
masked_statement += parts[i]
masked_statement += ","
if right_answer and wrong_answer:
masked_statement += masked_portion
masked_examples[key] = []
for entity_pair in random.sample(fictitious_entities,
num_entity_trials):
new_masked_statement = re.sub(r"\bA\b", entity_pair[0],
masked_statement)
new_masked_statement = re.sub(
r"\bB\b", entity_pair[1],
new_masked_statement).capitalize()
masked_examples[key].append(
(new_masked_statement, right_answer, wrong_answer))
return masked_examples