Code for the paper:
Learning Symbolic Rules for Reasoning in Quasi-Natural Language
Transactions on Machine Learning Research (TMLR), 2023
Kaiyu Yang and Jia Deng
@article{yang2023metaqnl,
title={Learning Symbolic Rules for Reasoning in Quasi-Natural Language},
author={Yang, Kaiyu and Deng, Jia},
journal={Transactions on Machine Learning Research (TMLR)},
year={2023},
}
julia --project=. -e 'import Pkg; Pkg.instantiate()'
.julia --project=. --color=yes docs/make.jl
will build the documentation at docs/build/
.
Use scripts/train.jl for training. Datasets will be downloaded automatically. Run julia --project=. scripts/train.jl --help
for command line options.
julia --project=. scripts/train.jl --dataset MiniSCAN --weight-candidate 0.4 --weight-existing 0.3 --maxsat-solver Z3
The simple
split:
julia --project=. scripts/train.jl --dataset SCAN --split simple --num-train-examples 400 --weight-candidate 0.15 --weight-existing 0.15 --maxsat-solver Z3
The length
split:
julia --project=. scripts/train.jl --dataset SCAN --split length --num-train-examples 400 --weight-candidate 0.15 --weight-existing 0.15 --maxsat-solver Z3
The addprim_jump
split:
julia --project=. scripts/train.jl --dataset SCAN --split addprim_jump --num-train-examples 400 --weight-candidate 0.15 --weight-existing 0.15 --maxsat-solver Z3
The addprim_turn_left
split:
julia --project=. scripts/train.jl --dataset SCAN --split addprim_turn_left --num-train-examples 400 --weight-candidate 0.15 --weight-existing 0.15 --maxsat-solver Z3
julia --project=. scripts/train.jl --dataset RuleTaker --split depth-1 --num-train-examples 10000 --weight-candidate 0.5 --weight-existing 0.5 --maxsat-solver Z3
julia --project=. scripts/train.jl --dataset RuleTaker --split depth-3 --num-train-examples 10000 --weight-candidate 0.25 --weight-existing 0.25 --maxsat-solver Z3 --lambda-provable 1.28 --lambda-unprovable 1.28
julia --project=. scripts/train.jl --dataset Sigmorphon --lang spanish --split hard --copy 0 --weight-candidate 1.0 --weight-existing 1.0 --num-epochs 8