def main():
"""
example:
"sarah discovered the vase that the dog might take ." vs. "sarah discovered what the dog might take the vase ."
"""
nouns_s = get_legal_words(tag='NN')
excluded_verbs_base = ('put', 'run', 'say', 'be', 'give', 'tell', 'live')
verbs_base = get_legal_words(tag='VB', exclude=excluded_verbs_base)
excluded_verbs_past = ('started', 'let', 'told')
verbs_past = get_legal_words(tag='VBD', exclude=excluded_verbs_past)
names_ = (configs.Dirs.legal_words / 'names.txt').open().read().split()
names = find_counterbalanced_subset(names_,
min_size=10,
max_size=len(names_))
animates_ = (configs.Dirs.legal_words /
'animates.txt').open().read().split()
animates = find_counterbalanced_subset(animates_,
min_size=8,
max_size=len(animates_))
def add_preposition_after_vb(v: str):
if v == 'play':
return 'play with'
elif v == 'point':
return 'point to'
elif v == 'turn':
return 'turn to'
elif v == 'work':
return 'work with'
else:
return v
while True:
# random choices
slot2filler = {
'name': random.choice(names),
'nn1': random.choice(nouns_s),
'nn2': random.choice(animates),
'vbd': random.choice(verbs_past),
'vbd2': random.choice(verbs_past), # used in template2 only
'vb': random.choice(verbs_base), # used in template 1 only
}
slot2filler['vb'] = add_preposition_after_vb(slot2filler['vb'])
yield template1['b'].format(**slot2filler) # bad
yield template1['g'].format(**slot2filler) # good
yield template2['b'].format(**slot2filler) # bad
yield template2['g'].format(**slot2filler) # good
def main():
"""
example:
"sam found one purple dog and karen revealed more ." vs. "sam found one dog and karen revealed more purple."
"""
vbds = get_legal_words(tag='VBD')
adjectives = get_legal_words(tag='JJ')
nouns_mass = (configs.Dirs.legal_words /
'nouns_mass.txt').open().read().split()
nouns_s = get_legal_words(tag='NN', exclude=tuple(nouns_mass))
animates_ = (configs.Dirs.legal_words /
'animates.txt').open().read().split()
animates = find_counterbalanced_subset(animates_,
min_size=8,
max_size=len(animates_))
names_ = (configs.Dirs.legal_words / 'names.txt').open().read().split()
names = find_counterbalanced_subset(names_,
min_size=10,
max_size=len(names_))
determiners = ['a', 'the', 'this', 'some', 'that'] + ['your', 'his', 'her']
number_words = ['several', 'more', 'two', 'three',
'a lot more'] # , 'some']
while True:
# random choices
slot2filler = {
'name1': random.choice(names),
'name2': random.choice(names),
'nn': random.choice(nouns_s),
'nn2': random.choice(animates),
'vbd': random.choice(vbds),
'det': random.choice(determiners),
'jj': random.choice(adjectives),
'number': random.choice(number_words),
}
yield template1['b'].format(**slot2filler) # bad
yield template1['g'].format(**slot2filler) # good
yield template2['b'].format(**slot2filler) # bad
yield template2['g'].format(**slot2filler) # good
def get_legal_words(tag: str,
second_tag: Optional[str] = None, # also counterbalance list of other word forms (e.g. plural)
seed: int = configs.Data.seed,
exclude: Optional[Tuple[str, ...]] = None,
verbose: bool = False,
) -> Union[List[str], List[Tuple[str, str]]]:
print(f'Obtaining counterbalanced subset of legal words with tag={tag} and second_tag={second_tag}')
# get words with requested tag and order
df_legal = pd.read_csv(configs.Dirs.legal_words / f'{tag}.csv')
bool_ids = df_legal['is_legal'].astype(bool).tolist()
first_forms_ = df_legal['word'][bool_ids].tolist()
# exclude any words ?
if exclude:
first_forms_ = [w for w in first_forms_ if w not in exclude]
# also counterbalance 2nd forms of words ?
if second_tag is None:
second_forms_ = None
elif second_tag == 'NNP':
plural = inflect.engine()
second_forms_ = [plural.plural(w) for w in first_forms_]
elif second_tag.startswith('VB'):
lemmas = [getLemma(w, upos='VERB')[0] for w in first_forms_]
second_forms_ = [getInflection(lemma, tag=second_tag)[0] for lemma in lemmas] # requires lemma as input
else:
raise AttributeError('Invalid arg to second_tag')
# remove words if their 2nd form is not in vocab or if it is identical to 1st form
if second_tag is not None:
first_forms = []
second_forms = []
for w1, w2 in zip(first_forms_, second_forms_):
if w2 in vocab and w2 != w1:
first_forms.append(w1)
second_forms.append(w2)
if verbose:
print(f'Included {w1:<12} and {w2:<12}')
assert first_forms
assert second_forms
else:
first_forms = first_forms_
second_forms = second_forms_
# find subset of words such that their total corpus frequencies are approx equal across corpora
num_words_in_sample = configs.Data.tag2num_words[tag]
res = find_counterbalanced_subset(first_forms,
min_size=num_words_in_sample,
max_size=num_words_in_sample+100,
second_forms=second_forms,
seed=seed,
verbose=verbose,
)
return res
def main():
"""
example:
"he made the van this challenge ." vs. "the van made he this challenge ."
"""
# counterbalance both forms of verb as different forms are the contrast
vbds = [
'brought',
'made',
'built',
'gave',
'showed',
]
nouns_s = get_legal_words(tag='NN')
animates_ = (configs.Dirs.legal_words /
'animates.txt').open().read().split()
animates = find_counterbalanced_subset(animates_,
min_size=8,
max_size=len(animates_))
personal_pronouns_obj = ['me', 'him', 'her', 'us',
'them'] # in the objective case
personal_pronouns_subj = ['i', 'he', 'she', 'we',
'they'] # in the subjective case
determiners = ['a', 'one', 'this', 'that', 'the', 'my', 'his', 'her']
vowels = {'a', 'e', 'i', 'o', 'u'}
while True:
vbd = random.choice(vbds) # template 1
# random choices
slot2filler = {
'nn': random.choice(animates),
'nn2': random.choice(nouns_s),
'det': random.choice(determiners),
'prp_obj': random.choice(personal_pronouns_obj),
'prp_subj': random.choice(personal_pronouns_subj),
'vbd': vbd,
}
if slot2filler['det'] == 'a' and slot2filler['nn2'][0] in vowels:
slot2filler['det'] += 'n'
yield template1['b'].format(**slot2filler) # bad
yield template1['g'].format(**slot2filler) # good
def main():
"""
example:
"a documentary was there looking at dogs ." vs. "there was a documentary looking at dogs ."
note: this task is too difficult for babyBERTa
"""
nouns_s_and_p = get_legal_words(tag='NN', second_tag='NNP')
adjectives = get_legal_words(tag='JJ')
quantifiers = ['each', 'most', 'all', 'every']
copula_p = ['were', 'are', "were not", "aren't"]
copula_s = ['was', 'is', "was not", "isn't"]
vowels = {'a', 'e', 'i', 'o', 'u'}
gerunds_ = [
'looking',
'becoming',
'falling',
'leaving',
'eating',
'increasing',
'moving',
'opening',
'existing',
'containing',
'standing',
'changing',
'surrounding',
'adding',
'acting',
]
gerunds = find_counterbalanced_subset(gerunds_,
min_size=8,
max_size=len(gerunds_))
# a linker can be a preposition or determiner phrase
gerund2linker = {
'looking': 'like a',
'becoming': 'some kind of a',
'falling': 'on the',
'leaving': 'us by the',
'eating': 'one piece of this',
'increasing': 'the size of the',
'moving': 'to the',
'opening': 'the door to a',
'existing': 'without a',
'containing': 'a',
'standing': 'on top of a',
'changing': 'the',
'surrounding': 'the',
'adding': 'to the',
'acting': 'like a',
}
while True:
# random choices
noun_s, noun_p = random.choice(nouns_s_and_p)
adj = random.choice(adjectives)
quantifier = random.choice(quantifiers)
subj_s, sub_p = random.choice(nouns_s_and_p)
gerund = random.choice(gerunds)
# plural vs. singular copula
if quantifier in {'most', 'all'}:
copula = random.choice(copula_p)
subj1 = sub_p # for template 1
else:
copula = random.choice(copula_s)
subj1 = subj_s
# "a" vs. "an"
linker = gerund2linker[gerund]
if linker.endswith('a') and adj[0] in vowels:
linker += 'n'
# contrast is about word order
yield template1.format('there', copula, quantifier, subj1, gerund,
linker, adj, noun_s) # bad
yield template1.format(quantifier, subj1, copula, 'there', gerund,
linker, adj, noun_s) # good
def main():
"""
example:
"no cat can jump on more than two dogs ." vs. "no cat jump on at least two dogs ."
"""
nouns_s_and_p = get_legal_words(tag='NN', second_tag='NNP')
number_words_ = (configs.Dirs.legal_words /
"number_words.txt").open().read().split()
number_words = find_counterbalanced_subset(number_words_,
min_size=6,
max_size=len(number_words_))
quantifiers_g_b = [
('more than', 'at least'),
('fewer than', 'at most'),
]
animates_ = (configs.Dirs.legal_words /
'animates.txt').open().read().split()
animates = find_counterbalanced_subset(animates_,
min_size=8,
max_size=len(animates_))
verbs_ = [
'become',
'catch',
'leave',
'increase',
'move',
'open',
'exist',
'contain',
'stand',
'change',
'surround',
'carry',
'act',
]
verbs = find_counterbalanced_subset(verbs_,
min_size=8,
max_size=len(verbs_))
# a linker can be a preposition or determiner phrase
verb2linker = {
'become': None,
'catch': None,
'leave': None,
'increase': 'the size of',
'move': 'to',
'open': 'the door to',
'exist': 'without',
'contain': None,
'stand': 'on top of',
'change': None,
'surround': None,
'carry': None,
'act': 'like',
}
while True:
# random choices
animate = random.choice(animates)
noun_s, noun_p = random.choice(nouns_s_and_p)
number_word = random.choice(number_words)
quantifier_g, quantifier_b = random.choice(quantifiers_g_b)
verb = random.choice(verbs)
aux = random.choice(['can', 'could'])
verb_and_optional_linker = verb
if verb2linker[verb] is not None:
verb_and_optional_linker += ' ' + verb2linker[verb]
if number_word == 'one':
noun = noun_s
else:
noun = noun_p
yield template1.format(animate, aux, verb_and_optional_linker,
quantifier_b, number_word, noun) # bad
yield template1.format(animate, aux, verb_and_optional_linker,
quantifier_g, number_word, noun) # good
def main():
"""
example:
"is the bell ringing ?" vs "is the bell rings ?"
"""
# counterbalance both forms of verb as different forms are the contrast
vbgs_and_vbzs = get_legal_words(tag='VBG',
second_tag='VBZ',
exclude=('facing', 'naming', 'training',
'setting', 'meaning'))
vbs_and_vbzs = get_legal_words(tag='VB', second_tag='VBZ')
nouns_s = get_legal_words(tag='NN')
animates_ = (configs.Dirs.legal_words /
'animates.txt').open().read().split()
animates = find_counterbalanced_subset(animates_,
min_size=8,
max_size=len(animates_))
names_ = (configs.Dirs.legal_words / 'names.txt').open().read().split()
names = find_counterbalanced_subset(names_,
min_size=10,
max_size=len(names_))
def add_argument_after_vb(
v: str,
arg1: str,
arg2: str,
) -> str:
if v in {'saying', 'says', 'say'}:
return f'{v} something'
elif v in {'using', 'uses', 'use'}:
return f'{v} {arg1}'
elif v in {'telling', 'tells', 'tell'}:
return f'{v} me about {arg1}'
elif v in {'making', 'makes', 'make'}:
return f'{v} {arg1} something'
elif v in {'planning', 'plans', 'plan'}:
return f'{v} to do something with {arg1}'
elif v in {'taking', 'takes', 'take'}:
return f'{v} {arg1} away'
elif v in {'giving', 'gives', 'give'}:
return f'{v} {arg1} {arg2}'
elif v in {'falling', 'falls', 'fall'}:
return f'{v} in {arg1}'
elif v in {'showing', 'shows', 'show'}:
return f'{v} {arg1} to {arg2}'
elif v in {'seeing', 'sees', 'see'}:
return f'{v} how the {arg1} works'
elif v in {'finding', 'finds', 'find'}:
return f'{v} {arg1}'
elif v in {'coming', 'comes', 'come'}:
return f'{v} to {arg1}'
elif v in {'getting', 'gets', 'get'}:
return f'{v} {arg1}'
elif v in {'depending', 'depends', 'depend'}:
return f'{v} on {arg1}'
else:
return v
while True:
vbg1, vbz1 = random.choice(vbgs_and_vbzs) # template 1
vb2, vbz2 = random.choice(vbs_and_vbzs) # template 2
# sample argument once, so that the same argument is used by both bad and good sentences.
# note: pronouns don't get determiners, but nouns do
argument1 = random.choice(['you', 'him', 'her', 'it'] +
[f'the {nn}' for nn in nouns_s])
argument2 = random.choice(['you', 'him', 'her', 'it'] +
[f'the {nn}' for nn in nouns_s])
# random choices
slot2filler = {
'name': random.choice(names),
'nn': random.choice(nouns_s + animates),
'vb2': add_argument_after_vb(vb2, argument1, argument2),
'vbz2': add_argument_after_vb(vbz2, argument1, argument2),
'vbg1': add_argument_after_vb(vbg1, argument1, argument2),
'vbz1': add_argument_after_vb(vbz1, argument1, argument2),
}
yield template1['b'].format(**slot2filler) # bad
yield template1['g'].format(**slot2filler) # good
yield template2['b'].format(**slot2filler) # bad
yield template2['g'].format(**slot2filler) # good
def main():
"""
example:
"there was a documentary about dogs ." vs. "there was each documentary about dogs ."
"""
nouns_s_and_p = get_legal_words(tag='NN', second_tag='NNP')
adjectives = get_legal_words(tag='JJ')
quantifiers_good = ['a', 'no', 'some', 'many', 'few']
quantifiers_bad = ['each', 'most', 'all', 'every']
template1_subjects_s_and_p = [
('movie', 'movies'),
('book', 'books'),
('story', 'stories'),
('sign', 'signs'),
]
vowels = {'a', 'e', 'i', 'o', 'u'}
copula_p = ['were', 'are', "were not", "aren't"]
copula_s = ['was', 'is', 'was not', "isn't"]
names_ = (configs.Dirs.legal_words / 'names.txt').open().read().split()
names = find_counterbalanced_subset(names_,
min_size=10,
max_size=len(names_))
while True:
# random choices
noun_s, noun_p = random.choice(nouns_s_and_p)
adj = random.choice(adjectives)
quantifier_b = random.choice(quantifiers_bad)
quantifier_g = random.choice(quantifiers_good)
subj_s, sub_p = random.choice(template1_subjects_s_and_p)
name = random.choice(names)
# plural vs. singular copula
if quantifier_g in {'some', 'many', 'few'}:
copula = random.choice(copula_p)
subj1 = sub_p # for template 1
subj2 = noun_p # for template 2
else:
copula = random.choice(copula_s)
subj1 = subj_s
subj2 = noun_s
# "a" vs. "an"
if subj1[0] in vowels and quantifier_g == 'a':
quantifier_g = 'an'
if subj1[0] in vowels and quantifier_b == 'a':
quantifier_b = 'an'
if subj2[0] in vowels and quantifier_g == 'a':
quantifier_g = 'an'
if subj2[0] in vowels and quantifier_b == 'a':
quantifier_b = 'an'
# prevent double negation
if quantifier_g == 'no' and ('not' in copula or "n't" in copula):
copula = copula.replace(' not', '')
copula = copula.replace(" n't", '')
yield template1.format(copula, quantifier_b, subj1, adj, noun_p) # bad
yield template1.format(copula, quantifier_g, subj1, adj,
noun_p) # good
yield template2.format(copula, quantifier_b, subj2, name) # bad
yield template2.format(copula, quantifier_g, subj2, name) # good
def main():
"""
example:
sam questioned the dog that can hurt sara ." vs "sam questioned who the dog can hurt sara."
"""
nouns_s_and_p = get_legal_words(tag='NN', second_tag='NNP')
excluded_verbs_base = ('put', 'run', 'say', 'be', 'give', 'tell', 'live')
verbs_base = get_legal_words(tag='VB', exclude=excluded_verbs_base)
excluded_verbs_past = ('started', 'let', 'told')
verbs_past = get_legal_words(tag='VBD', exclude=excluded_verbs_past)
excluded_verbs_gerund = ('saying', )
verbs_gerund = get_legal_words(tag='VBG', exclude=excluded_verbs_gerund)
names_ = (configs.Dirs.legal_words / 'names.txt').open().read().split()
names = find_counterbalanced_subset(names_,
min_size=10,
max_size=len(names_))
def add_preposition_after_vb(v: str):
if v in {'acting', 'act'}:
return f'{v} like'
elif v in {
'standing', 'stand', 'falling', 'fall', 'depending', 'depend'
}:
return f'{v} on'
elif v in {'asking', 'ask', 'writing', 'write', 'thinking', 'think'}:
return f'{v} about'
elif v in {'swimming', 'swim', 'sleeping', 'sleep'}:
return f'{v} in'
elif v in {'driving', 'drive', 'coming', 'come', 'related', 'relate'}:
return f'{v} to'
elif v in {'flying', 'fly', 'working', 'work'}:
return f'{v} with'
else:
return v
while True:
# random choices
slot2filler = {
'name': random.choice(names),
'nn': random.choice(nouns_s_and_p)[0],
'nns': random.choice(nouns_s_and_p)[1],
'vbd': random.choice(verbs_past),
'vbg': random.choice(verbs_gerund), # used in template2 only
'vb': random.choice(verbs_base), # used in template 1 only
}
slot2filler['vb'] = add_preposition_after_vb(slot2filler['vb'])
slot2filler['vbd'] = add_preposition_after_vb(slot2filler['vbd'])
slot2filler['vbg'] = add_preposition_after_vb(slot2filler['vbg'])
yield template1['b'].format(**slot2filler) # bad
yield template1['g'].format(**slot2filler) # good
yield template2['b'].format(**slot2filler) # bad
yield template2['g'].format(**slot2filler) # good
def main():
"""
example:
"katherine will help herself do something" vs. "katerine will help himself do something"
"""
excluded_verbs_base = ('say', )
verbs_base = get_legal_words(tag='VB', exclude=excluded_verbs_base)
nouns_s = get_legal_words(tag='NN')
names_ = (configs.Dirs.legal_words / 'names.txt').open().read().split()
names = find_counterbalanced_subset(names_,
min_size=10,
max_size=len(names_))
auxiliaries = ['can', 'could', 'will', 'would', 'must', 'should']
def add_misc_after_prp(
prp: str,
v: str,
arg1: str,
) -> str:
if v in {
'take',
}:
return f'{prp} to {arg1}'
elif v in {'make', 'give', 'put'}:
return f'{prp} {arg1}'
elif v in {
'work',
}:
return f'{prp} on {arg1}'
elif v in {
'turn',
}:
return f'{prp} around'
elif v in {
'tell',
}:
return f'{prp} about {arg1}'
else:
return prp
def add_preposition_after_vb(v: str) -> str:
if v in {'work', 'study', 'live'}:
return f'{v} with'
elif v in {'point', 'run'}:
return f'{v} to'
elif v in {
'be',
}:
return f'{v} like'
else:
return v
while True:
vb = random.choice(verbs_base)
# random choices
slot2filler = {
'aux':
random.choice(auxiliaries),
'nn_m':
random.choice([name for name in names if name in names_m] +
['he', 'the man', 'a man', 'that man']),
'nn_f':
random.choice([name for name in names if name in names_f] +
['she', 'the woman', 'a woman', 'that woman']),
}
# sample argument once, so that the same argument is used by both bad and good sentences.
# note: pronouns don't get determiners, but nouns do
if vb == 'put':
argument1 = random.choice(['in danger', 'in this situation'])
else:
argument1 = random.choice([f'the {nn}' for nn in nouns_s])
# first, add some miscellaneous component
slot2filler['prp_m'] = add_misc_after_prp('himself', vb, argument1)
slot2filler['prp_f'] = add_misc_after_prp('herself', vb, argument1)
# second, add a preposition
slot2filler['vb'] = add_preposition_after_vb(vb)
yield template1['b'].format(**slot2filler) # bad
yield template1['g'].format(**slot2filler) # good
yield template2['b'].format(**slot2filler) # bad
yield template2['g'].format(**slot2filler) # good
# use negation only in non-question, in templates 3, and 4
if random.random() < 0.5:
slot2filler['aux'] += ' ' + 'not'
if random.random() < 0.1:
slot2filler['aux'] = 'did not'
yield template3['b'].format(**slot2filler) # bad
yield template3['g'].format(**slot2filler) # good
yield template4['b'].format(**slot2filler) # bad
yield template4['g'].format(**slot2filler) # good
def main():
"""
example:
"who should sarah hug after shocking the dog ?" vs "who should sarah hug the dog after shocking ?"
note: this task is too difficult for babyBERTa
"""
nouns_s = get_legal_words(tag='NN')
excluded_verbs_base = ('run', 'say', 'be', 'give', 'tell', 'live', 'force')
verbs_base = get_legal_words(tag='VB', exclude=excluded_verbs_base)
excluded_verbs_gerund = ('saying', )
verbs_gerund = get_legal_words(tag='VBG', exclude=excluded_verbs_gerund)
names_ = (configs.Dirs.legal_words / 'names.txt').open().read().split()
names = find_counterbalanced_subset(names_,
min_size=10,
max_size=len(names_))
pps = ['after', 'before', 'while', 'without']
def add_preposition_after_vb(v: str):
if v == 'related':
return 'related to'
elif v == 'acting':
return 'acting like'
elif v == 'put':
return 'put on'
elif v == 'work':
return 'work for'
elif v == 'sleeping':
return 'sleeping in'
elif v == 'standing':
return 'standing on'
elif v == 'depending':
return 'depending on'
elif v == 'flying':
return 'flying over'
elif v == 'falling':
return 'falling on'
elif v == 'asking':
return 'asking about'
elif v == 'swimming':
return 'swimming in'
elif v == 'asking':
return 'asking for'
elif v == 'coming':
return 'coming to'
else:
return v
while True:
# random choices
slot2filler = {
'name': random.choice(names),
'nn': random.choice(nouns_s),
'pp': random.choice(pps),
'vb': random.choice(verbs_base),
'vbg': random.choice(verbs_gerund),
}
slot2filler['vb'] = add_preposition_after_vb(slot2filler['vb'])
slot2filler['vbg'] = add_preposition_after_vb(slot2filler['vbg'])
yield template1['b'].format(**slot2filler) # bad
yield template1['g'].format(**slot2filler) # good
yield template2['b'].format(**slot2filler) # bad
yield template2['g'].format(**slot2filler) # good
def main():
"""
example:
"give me the frog ." vs. "the frog gives me ."
"""
# counterbalance both forms of verb as different forms are the contrast
vbs_and_vbzs_1 = [
('give', 'gives'),
]
vbs_and_vbzs_2 = [
('ask', 'asked'), # "asks" is not in vocab
('tell', 'tells'),
]
prps_obj = ['me', 'you', 'him', 'her', 'them']
conjunctions = ['when', 'but', 'with', 'and']
# TODO use a counterbalanced verb list
vbzs3_and_continuations = [ # contains a mix of past and present tense forms
# past tense form
('saw', '{prp_obj} there', '{prp_obj} by'),
('created', '{prp_obj}', '{det}'),
('told', '{prp_obj} about that', '{prp_obj} about {det}'),
('wrote', '{prp_obj} something', '{prp_obj} {det}'),
('wanted', '{prp_obj}', 'to'),
('asked', 'about {prp_obj}', '{prp_obj} about'),
('sold', '{prp_obj} that', 'that to'),
('changed', '{prp_obj}', '{det}'),
# present tense form
('looks', 'at {prp_obj}', 'at {det}'),
('plays', 'with {prp_obj}', 'with {det}'),
('thinks', 'about {prp_obj}', 'about {det}'),
('moves', 'fast', 'to'),
('works', 'well', '{conjunction}'),
]
adjectives = get_legal_words(tag='JJ')
nouns_s = get_legal_words(tag='NN')
animates_ = (configs.Dirs.legal_words /
'animates.txt').open().read().split()
animates = find_counterbalanced_subset(animates_,
min_size=8,
max_size=len(animates_))
personal_pronouns_obj = ['me', 'him', 'her', 'us',
'them'] # in the objective case
personal_pronouns_subj = ['i', 'he', 'she', 'we',
'they'] # in the subjective case
determiners = ['a', 'one', 'the', 'my', 'his',
'some'] # do not include "this" or "that" or "her"
vowels = {'a', 'e', 'i', 'o', 'u'}
while True:
vb1, vbz1 = random.choice(vbs_and_vbzs_1) # template 1
vb2, vbz2 = random.choice(vbs_and_vbzs_2) # template 2
vbz3, cont_g, cont_b = random.choice(
vbzs3_and_continuations) # template 3
# good and bad continuations
prp_obj = random.choice(prps_obj)
conjunction = random.choice(conjunctions)
cont_g = cont_g.format(prp_obj=prp_obj)
cont_b = cont_b.format(prp_obj=prp_obj,
det=random.choice(determiners),
conjunction=conjunction)
# random choices
slot2filler = {
'det': random.choice(determiners),
'jj': random.choice(adjectives),
'nn': random.choice(animates),
'nn2': random.choice(nouns_s),
'prp_obj': random.choice(personal_pronouns_obj),
'prp_subj': random.choice(personal_pronouns_subj),
'vb1': vb1,
'vbz1': vbz1,
'vb2': vb2,
'vbz2': vbz2,
'vbz3': vbz3,
'cont_g': cont_g,
'cont_b': cont_b,
}
if slot2filler['det'] == 'a' and slot2filler['nn2'][0] in vowels:
slot2filler['det'] += 'n'
yield template1['b'].format(**slot2filler) # bad
yield template1['g'].format(**slot2filler) # good
yield template2['b'].format(**slot2filler) # bad
yield template2['g'].format(**slot2filler) # good
yield template3['b'].format(**slot2filler) # bad
yield template3['g'].format(**slot2filler) # good
def main():
"""
example:
"who must sarah and the dog kiss ?" vs "who must sarah kiss and the dog ?"
"""
excluded_verbs_base = ('run', 'be', 'live', 'force', 'order')
verbs_base = get_legal_words(tag='VB', exclude=excluded_verbs_base)
excluded_verbs_gerund = ('',)
verbs_gerund = get_legal_words(tag='VBG', exclude=excluded_verbs_gerund)
animates_ = (configs.Dirs.legal_words / 'animates.txt').open().read().split()
animates = find_counterbalanced_subset(animates_, min_size=8, max_size=len(animates_))
names_ = (configs.Dirs.legal_words / 'names.txt').open().read().split()
names = find_counterbalanced_subset(names_, min_size=10, max_size=len(names_))
def add_preposition_after_vb(v: str,
arg: str,
):
if v == 'related':
return 'related to'
elif v == 'put':
return 'put on'
elif v == 'work':
return 'work for'
elif v == 'acting':
return 'acting like'
elif v == 'sleeping':
return 'sleeping in'
elif v == 'falling':
return 'falling on'
elif v == 'looking':
return 'looking for'
elif v == 'running':
return 'running to'
elif v == 'talking':
return 'talking about'
elif v == 'thinking':
return 'thinking about'
elif v == 'reaching':
return 'reaching for'
elif v == 'work':
return f'work {arg}'
else:
return v
while True:
# random choices
slot2filler = {
'name': random.choice(names),
'nn': random.choice(animates),
'vb': random.choice(verbs_base),
'vbg': random.choice(verbs_gerund),
}
arg = random.choice(["him", "her", "them", "us"])
slot2filler['vb'] = add_preposition_after_vb(slot2filler['vb'], arg)
slot2filler['vbg'] = add_preposition_after_vb(slot2filler['vbg'], arg)
# exclude bad combinations that involve "who", e.g. "saying who"
if slot2filler['vbg'] not in {'saying', 'drinking', 'eating', 'open'}\
and slot2filler['vb'] not in {'need', 'feel', 'open'}:
if slot2filler['vb'] == 'tell':
slot2filler['vb'] = 'tell something'
yield template1['b'].format(**slot2filler) # bad
yield template1['g'].format(**slot2filler) # good
yield template2['b'].format(**slot2filler) # bad
yield template2['g'].format(**slot2filler) # good
yield template3['b'].format(**slot2filler) # bad
yield template3['g'].format(**slot2filler) # good
yield template4['b'].format(**slot2filler) # bad
yield template4['g'].format(**slot2filler) # good
def main():
"""
example:
"sarah laughs" vs. "sarah gives"
"""
# we need a lot of verbs here, so temporarily reduce restrictions imposed by counterbalancing
tmp1 = configs.Data.tag2num_words['VB']
tmp2 = configs.Data.tag2num_words['VBZ']
tmp3 = configs.Data.tag2num_words['VBD']
tmp4 = configs.Data.bias_tolerance
configs.Data.tag2num_words['VB'] = 30
configs.Data.tag2num_words['VBZ'] = 50
configs.Data.tag2num_words['VBD'] = 50
configs.Data.bias_tolerance = 7000
vbs = get_legal_words(tag='VB', exclude=('fit', 'come', 'point'))
vbzs = get_legal_words(tag='VBZ', exclude=('points', ))
vbds = get_legal_words(tag='VBD', exclude=('fit', 'dropped', 'signed', 'formed', 'managed'))
configs.Data.tag2num_words['VB'] = tmp1
configs.Data.tag2num_words['VBZ'] = tmp2
configs.Data.tag2num_words['VBD'] = tmp3
configs.Data.bias_tolerance = tmp4
animates_ = (configs.Dirs.legal_words / 'animates.txt').open().read().split()
animates = find_counterbalanced_subset(animates_, min_size=8, max_size=len(animates_))
prps_s = ['she', 'he', 'it']
prps_p = ['we', 'they']
prps = prps_s + prps_p
aux_s = ['does']
auxiliaries = ['could', 'can', 'would', 'will', 'did'] + aux_s
determiners = ['the', 'this', 'some', 'that', 'every'] + ['your', 'his', 'her']
names_ = (configs.Dirs.legal_words / 'names.txt').open().read().split()
names = find_counterbalanced_subset(names_, min_size=10, max_size=len(names_))
vbs_intransitive = [
'run',
'work',
'turn',
'eat',
'live',
'read',
'trade',
'play',
'know',
'study',
'think',
'change',
]
vbzs_intransitive = [
'moves',
'lives',
'lies',
'knows',
'waves',
'changes',
'works',
'dies',
'leads',
'appears',
'thinks',
'falls',
'matters',
'turns',
'stands',
'stands',
'runs',
'calls',
'races',
]
vbds_intransitive = [
'occurred',
'married',
'moved',
'looked',
'changed',
'finished',
'grew',
'broke',
'started',
'improved',
'worked',
'thought',
'came',
'tried',
'read',
'lost',
'knew',
'lived',
'accepted',
'developed',
'joined',
'joined',
'decided',
'learned',
'occurred',
'happened',
'fell',
'refused',
'returned',
]
vbs_intransitive = vbs_intransitive
vbs_transitive = [v for v in vbs if v not in vbs_intransitive]
vbzs_transitive = [v for v in vbzs if v not in vbzs_intransitive]
vbzs_or_vbds_intransitive = vbzs_intransitive + vbds_intransitive
vbzs_or_vbds_transitive = [v for v in vbzs + vbds if v not in vbzs_or_vbds_intransitive]
while True:
# random choices
slot2filler = {
'nn1': random.choice(animates + names + prps),
'aux': random.choice(auxiliaries),
'vbz_or_vbd_intransitive': random.choice(vbzs_or_vbds_intransitive),
'vbz_or_vbd_transitive': random.choice(vbzs_or_vbds_transitive),
'vb_intransitive': random.choice(vbs_intransitive),
'vb_transitive': random.choice(vbs_transitive),
}
# handle exception: "occurred" and "happened" cannot have animate subject
if slot2filler['vbz_or_vbd_intransitive'] in ['occurred', 'happened'] and\
slot2filler['nn1'] not in ['it', 'that', 'this']:
continue
# add determiner to animate noun
if slot2filler['nn1'] in animates:
slot2filler['nn1'] = random.choice(determiners) + ' ' + slot2filler['nn1']
# do not use template 1 with plural pronoun and VBZ
if not (slot2filler['nn1'] in prps_p and
slot2filler['vbz_or_vbd_intransitive'] in vbzs_intransitive or
slot2filler['vbz_or_vbd_transitive'] in vbzs_transitive):
yield template1['b'].format(**slot2filler) # bad
yield template1['g'].format(**slot2filler) # good
# do not use template 2 with plural pronoun and singular aux (e.g. "does")
if not (slot2filler['nn1'] in prps_p and
slot2filler['aux'] in aux_s):
yield template2['b'].format(**slot2filler) # bad
yield template2['g'].format(**slot2filler) # good
def main():
"""
example:
"only sarah could ever talk." vs. "even sarah could ever talk"
"""
vbzs = get_legal_words(tag='VBZ', exclude=('happens', 'says', ))
vbs = get_legal_words(tag='VB')
nouns_s = get_legal_words(tag='NN')
animates_ = (configs.Dirs.legal_words / 'animates.txt').open().read().split()
animates = find_counterbalanced_subset(animates_, min_size=8, max_size=len(animates_))
names_ = (configs.Dirs.legal_words / 'names.txt').open().read().split()
names = find_counterbalanced_subset(names_, min_size=10, max_size=len(names_))
determiners = ['the', 'this', 'some', 'that'] + ['your', 'his', 'her']
auxiliaries = ['could', 'can', 'would', 'will']
def add_argument_after_vb(v: str,
argument1: str,
) -> str:
if v in {'thinks', 'reads'}:
return f'{v} about'
elif v in {'lives', 'falls', 'is', 'be'}:
return f'{v} in'
elif v in {'stands', 'turns'}:
return f'{v} on'
elif v in {'acts', 'looks'}:
return f'{v} like'
elif v in {'goes', 'comes'}:
return f'{v} to'
elif v in {'gives', 'gives'}:
return f'{v} {argument1}'
elif v in {'plays', 'play', 'shows', 'show', 'tells', 'tell'}:
return f'{v} {argument1}'
else:
return v
while True:
arg1 = random.choice(['him', 'her'])
vbz = random.choice(vbzs)
vb = random.choice(vbs)
# random choices
slot2filler = {
'nn1': random.choice(names + animates),
'nn2': random.choice(nouns_s),
'vbz': add_argument_after_vb(vbz, arg1),
'vb': add_argument_after_vb(vb, arg1),
'det': random.choice(determiners),
'aux': random.choice(auxiliaries)
}
# add determiner to animate noun
if slot2filler['nn1'] in animates:
slot2filler['nn1'] = random.choice(determiners) + ' ' + slot2filler['nn1']
yield template1['b'].format(**slot2filler) # bad
yield template1['g'].format(**slot2filler) # good
yield template2['b'].format(**slot2filler) # bad
yield template2['g'].format(**slot2filler) # good
def main():
"""
example:
"has sam ever worried sarah ?" vs. "jane has ever worried sarah ."
"""
vbs = get_legal_words(tag='VB')
nouns_s = get_legal_words(tag='NN')
animates_ = (configs.Dirs.legal_words /
'animates.txt').open().read().split()
animates = find_counterbalanced_subset(animates_,
min_size=8,
max_size=len(animates_))
names_ = (configs.Dirs.legal_words / 'names.txt').open().read().split()
names = find_counterbalanced_subset(names_,
min_size=10,
max_size=len(names_))
auxiliaries = ['does', 'will', 'could', 'did', 'should', 'would']
determiners = ['a', 'the', 'this', 'some', 'that'] + ['your', 'his', 'her']
def add_argument_after_vb(
v: str,
arg1: str,
arg2: str,
) -> str:
if v in {'say'}:
return f'{v} something'
elif v in {'read'}:
return f'{v} a book'
elif v in {'play'}:
return f'{v} with {arg1}'
elif v in {
'use', 'find', 'get', 'be', 'order', 'need', 'have', 'control',
'want', 'free', 'keep'
}:
return f'{v} {arg1}'
elif v in {'tell'}:
return f'{v} me about {arg1}'
elif v in {'plan'}:
return f'{v} to do something with {arg1}'
elif v in {'take'}:
return f'{v} {arg1} away'
elif v in {'give', 'show', 'present'}:
return f'{v} {arg1} to {arg2}'
elif v in {'put'}:
return f'{v} {arg1} on {arg2}'
elif v in {'fall'}:
return f'{v} in {arg1}'
elif v in {'see'}:
return f'{v} how the {arg1} works'
elif v in {'come'}:
return f'{v} to {arg1}'
else:
return v
while True:
# sample argument once, so that the same argument is used by both bad and good sentences.
# note: pronouns don't get determiners, but nouns do
argument1 = random.choice(['you', 'him', 'her', 'it'] +
[f'the {nn}' for nn in nouns_s])
argument2 = random.choice(['you', 'him', 'her', 'it'] +
[f'the {nn}' for nn in nouns_s])
vb = random.choice(vbs)
# random choices
slot2filler = {
'name': random.choice(names),
'nn': random.choice(animates),
'vb': add_argument_after_vb(vb, argument1, argument2),
'aux': random.choice(auxiliaries),
'det': random.choice(determiners),
}
if slot2filler['aux'] in {'did', 'does'} and vb == 'be':
continue
yield template1['b'].format(**slot2filler) # bad
yield template1['g'].format(**slot2filler) # good
yield template2['b'].format(**slot2filler) # bad
yield template2['g'].format(**slot2filler) # good
def main():
"""
example:
"we can help him do something" vs. "we can help himself do something"
"""
# counterbalance both forms of verb as different forms are the contrast
excluded_verbs_base = ('say', 'live')
verbs_base = get_legal_words(tag='VB', exclude=excluded_verbs_base)
nouns_s = get_legal_words(tag='NN')
prps_obj_and_poss = [
('him', 'his'),
('her', 'hers'),
('us', 'our'),
('them', 'theirs'),
]
animates_ = (configs.Dirs.legal_words /
'animates.txt').open().read().split()
animates = find_counterbalanced_subset(animates_,
min_size=8,
max_size=len(animates_))
auxiliaries = ['can', 'could', 'will', 'would', 'must', 'should']
def add_misc_after_prp(
prp: str,
v: str,
arg1: str,
) -> str:
if v in {'take'}:
return f'{prp} to {arg1}'
elif v in {'make'}:
return f'{prp} do {arg1}'
elif v in {'work', 'put'}:
return f'{prp} on {arg1}'
elif v in {'turn'}:
return f'{prp} around'
elif v in {'tell'}:
return f'{prp} about {arg1}'
else:
return prp
def add_preposition_after_vb(v: str) -> str:
if v in {'work', 'study'}:
return f'{v} with'
elif v in {'point', 'run'}:
return f'{v} to'
elif v in {'be'}:
return f'{v} like'
else:
return v
while True:
prp_obj, prp_poss = random.choice(prps_obj_and_poss)
# random choices
slot2filler = {
'aux': random.choice(auxiliaries),
'prp_poss': prp_poss,
'prp_obj': prp_obj,
'nn': random.choice(animates),
'vb': random.choice(verbs_base),
}
# sample argument once, so that the same argument is used by both bad and good sentences.
# note: pronouns don't get determiners, but nouns do
argument1 = random.choice([f'the {nn}' for nn in nouns_s])
# first, add some miscellaneous component
slot2filler['prp_poss'] = add_misc_after_prp(prp_poss,
slot2filler['vb'],
argument1)
slot2filler['prp_obj'] = add_misc_after_prp(prp_obj, slot2filler['vb'],
argument1)
# lastly, add a preposition
slot2filler['vb'] = add_preposition_after_vb(slot2filler['vb'])
yield template1['b'].format(**slot2filler) # bad
yield template1['g'].format(**slot2filler) # good
def main():
"""
example:
"a big dog fell down the stairs ." vs. "a big dog fallen down the stairs ."
"""
vocab = get_vocab_words()
modifiers = [
'over there', 'some time ago', 'this morning', 'at home', 'last night'
]
names_ = (configs.Dirs.legal_words / 'names.txt').open().read().split()
names = find_counterbalanced_subset(names_,
min_size=10,
max_size=len(names_))
vbds_vbns_args = [
('arose', 'arisen', ['']),
# optional arguments
('knew', 'known', ['a lot of things', 'she could do it']),
('saw', 'seen', ['a bird', 'a shape', 'something']),
('began', 'begun', ['to work']),
('fell', 'fallen', ['down the stairs']),
('flew', 'flown', ['into the sky', 'away']),
('drove', 'driven', [
'out of the garage', 'down the road', 'with one wheel',
'without looking'
]),
('grew', 'grown', [
'quickly',
]),
('hid', 'hidden', ['from view', 'behind the bush']),
('rose', 'risen', ['from bed']),
('swore', 'sworn', ['not to do it again']),
('drank', 'drunk', ['some juice', 'the soup', 'your coffee']),
('ate', 'eaten', ['a lot', 'more than me', 'some ice cream']),
('drew', 'drawn', ['a picture', 'a map', 'a round circle']),
('wrote', 'written',
['a story', 'a note', 'into a book', 'with a large pen']),
('sang', 'sung', [
'a nice song', 'in the theater', 'with a pretty voice',
'my favorite song'
]),
('spoke', 'spoken',
['very fast', 'to me', 'about many things', 'without thinking']),
('came', 'come',
['to the store', 'just in time', 'when we needed her', 'too late']),
# transitive
('was', 'been', ['here', 'alone', 'afraid']),
('beat', 'beaten', ['the dough', 'a little boy', 'their pet']),
('became', 'become', ['angry', 'very different', 'someone else']),
('bit', 'bitten',
['her own tongue', 'into the cake', 'off a big chunk']),
('blew', 'blown', [
'out the candle',
'away the dirt',
]),
('chose', 'chosen', [
'the best option',
'the good one',
]),
('did', 'done',
['nothing wrong', 'something bad', 'the best she could ']),
('forgave', 'forgiven', ['her', 'the child', 'him']),
('gave', 'given', [
'a book to a student', 'something sweet to the baby',
'money to the man'
]),
('rode', 'ridden', ['a horse', 'a cart', 'in the front seat', 'away']),
('shook', 'shaken', ['the plate', 'the table', 'the bowl']),
('strode', 'stridden', ['']),
('took', 'taken', ['a paper', 'some food', 'the bell', 'it', 'them']),
('threw', 'thrown',
['the trash out', 'the paper ball', 'some away', 'his ball']),
]
while True:
# random choices
name = random.choice(names)
mod = random.choice(modifiers)
vbd, vbn, args = random.choice(vbds_vbns_args)
arg = random.choice(args)
if (vbd not in vocab or vbn not in vocab) or vbd == vbn:
# print(f'"{verb_base:<22} excluded due to some forms not in vocab')
continue
if arg == '':
continue
# vbd is correct
yield template.format(name, vbn, arg, mod) # bad
yield template.format(name, vbd, arg, mod) # good
# vbn is correct
yield template.format(name, 'had ' + vbd, arg, mod)
yield template.format(name, 'had ' + vbn, arg, mod)
