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Compositional Generalization without Trees using Multiset Tagging and Latent Permutations

This is the official code for our ACL 2023 paper Compositional Generalization without Trees using Multiset Tagging and Latent Permutations.

Usage

Installation

Clone this repository, and unzip data.zip. Then create a conda environment with Python 3.8:

conda create -n f-r python=3.8
conda activate f-r

And install PyTorch 1.8:

# CUDA 10.2
pip3 install torch==1.8.2 torchvision==0.9.2 torchaudio==0.8.2 --extra-index-url https://download.pytorch.org/whl/lts/1.8/cu102

# CUDA 11.1
pip3 install torch==1.8.2 torchvision==0.9.2 torchaudio==0.8.2 --extra-index-url https://download.pytorch.org/whl/lts/1.8/cu111

# CPU Only
pip3 install torch==1.8.2 torchvision==0.9.2 torchaudio==0.8.2 --extra-index-url https://download.pytorch.org/whl/lts/1.8/cpu

Then install the remaining requirements (this may take a while):

pip install -r requirements.txt

Place GloVe embeddings into ~/downloads/ or the adapt config files (configs/) accordingly to point to where you saved the GloVe embeddings.

Training a model

A configuration file describes a model, the data you want to train on and all hyperparameters. Pick a configuration file from configs/ Ensure that [train|dev|test]_data_path point to the right data; also make sure that pretrained_file points to your copy of the GloVe embeddings. Then run We train two models, one for predicting multisets, and after that the one for predicting permutations.

To train both models, run

python train_both.py [path to where model will be saved] [multiset model config] [permutation model config]

For example, to train a model for COGS (using the lexicon), run

mkdir -p models/
python train_both.py models/multiset_cogs_lexicon_model configs/cogs/cogs_lexicon_freq.jsonnet configs/cogs/cogs_lexicon_perm.jsonnet

This will create two directories, models/multiset_cogs_lexicon_model_freq and models/multiset_cogs_lexicon_model_perm containing the corresponding two models. I recommend not moving these directories (otherwise paths must be adjusted). You can also add the additional argument stage_1 or stage_2 to train only one of the models.

If you want the experiment to be logged by neptune.ai, make sure that trainer.callbacks contains an entry like this:

{ "type": "neptune", "project_name": "[project name]" }

I you want to tune hyperparameters automatically, you can use this fork of allentune. Files with search spaces for hyperparameters are in hparam_search/.

Evaluation of a model

You can compute evaluation metrics on some data like this:

allennlp evaluate [path/to/model.tar.gz] [path/to/data.tsv] --include-package fertility

Be sure to use a model trained for predicting permutations (ending in _perm) to get evaluation results on sequence outputs (and not multisets). If you want to use a cuda device, add --cuda-device [device_id] to that. See allennlp evaluate --help for more options such as saving the metrics without rounding.

If you want to save predictions of the model on same data (e.g. for error analysis), use this:

allennlp predict [path/to/model.tar.gz] [path/to/data.tsv] --include-package fertility --use-dataset-reader --output-file [outout-file.jsonl]

Inference for Relaxed Permutations

We introduce a generalization of the Sinkhorn-Knopp algorithm for inference with relaxed permutations that are parameterized with jumps.

You can find the code for this algorithm in fertility/reordering/bregman_for_perm.py, which is a standalone file that also includes a simple example. Feel free to reach out if you have any questions!

Citation

@inproceedings{lindemann-etal-2023a-compositional,
    title = "Compositional Generalization without Trees using Multiset Tagging and Latent Permutations",
    author = "Lindemann, Matthias  and
      Koller, Alexander  and
      Titov, Ivan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.810",
}