Tensor Recursive Model

We provide an extension neural and probabilistic models of recursive models for tree-structure data based on tensor theory.

List of publication based on this repository:

"Generalising Recursive Neural Models by Tensor Decomposition",
Daniele Castellana, Davide Bacciu,
International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020
"Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data",
Daniele Castellana, Davide Bacciu,
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 2020
"Learning from Non-Binary Constituency Trees via Tensor Decomposition",
Daniele Castellana, Davide Bacciu,
Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain, 2020

Code Structure

The code is structured as follows:

data contains the BoolSent dataset
models contains the implementation of the recursive models (both probabilistic and neural);
preprocessing contains the code to preprocess the datasets;
tasks contains all the files to execute the experiments; there is a readme files for each task;
tree_utils contains utils code.

Also, two other repositories are used:

exputils provides the utils to run an experiment, train a model, parse configuration, etc..
thlogprob provides a minimal library to handle distribution using pytorch.

See the readme for more information.

download the raw data (see next section);
in NLP tasks, sentences should be parsed running the command:

python tasks/task_name/parse_raw_data raw_data_folder output_folder
Run the preprocessor using the command:

python preprocess.py --config-file preproc_config_file,
where preproc_config_file can be found in the folder tasks/task_name/config_files
Run the experiment using the command:

python run.py --config-file run_config_file,
where run_config_file can be found in the folder tasks/task_name/config_files.