Unigram is a library for random (depth first) generation with context-sensitive grammars (but also context free grammars) for synthetic data creation.
One particularity is the option to generate in multiple languages in parallel (for example, tptp and pseudo-english).
Example with LogicNLI grammar:
pip install unigram
from unigram import init_grammar, generate
def LogicNLI():
ADJECTIVES = ['rich', 'quiet', 'old', 'tall', 'kind', 'brave', 'wise',
'happy', 'strong', 'curious', 'patient', 'funny', 'generous', 'humble']
# (We selected adjectives with no clear semantic interference)
NAMES = ['mary', 'paul', 'fred', 'alice', 'john', 'susan', 'lucy']
R = init_grammar(['tptp','eng'])
R('start(' + ','.join(['rule']*16) + ',' + ','.join(['fact']*8) + ')',
'&\n'.join([f'({i})' for i in range(24)]),
'\n'.join([f'{i}' for i in range(24)]))
R('hypothesis(person,a)', '1(0)', '0 is 1')
for a in ADJECTIVES:
R('adj', a)
R('adj', f'~{a}', f'not {a}', weight=0.2)
R('property(adj,adj)', '(0(?)&1(?))', 'both 0 and 1')
R('property(adj,adj)', '(0(?)|1(?))', '0 or 1')
R('property(adj,adj)', '(0(?)<~>1(?))', 'either 0 or 1', weight=0.5)
R('property(adj)', '0(?)', '0')
R('rule(property,property)', '![X]:(0[?←X]=>1[?←X])',
'everyone who is 0 is 1')
R('rule(property,property)', '![X]:(0[?←X]<=>1[?←X])',
'everyone who is 0 is 1 and vice versa')
for p in NAMES:
R('person', p)
R('fact(person,property)', '1[?←0]', '0 is 1')
R('fact(property)', '?[X]:(0[?←X])', 'someone is 0', weight=0.2)
R('rule(fact,fact)', '(0)=>(1)', 'if 0 then 1')
R('rule(fact,fact)', '(0)<=>(1)', 'if 0 then 1 and vice versa')
return R
eng, tptp = "eng","tptp"
grammar = LogicNLI()
x=generate(grammar)
print(x@eng)
print(x@tptp)Citation:
@inproceedings{sileo-2024-scaling,
title = "Scaling Synthetic Logical Reasoning Datasets with Context-Sensitive Declarative Grammars",
author = "Sileo, Damien",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.301/",
doi = "10.18653/v1/2024.emnlp-main.301",
pages = "5275--5283",
}