Rotation Document Model

In this repository we provide C and Python code to generate embeddings proposed in our paper. [paper]

Here is the list of libraries you need to install to execute the code

C version:

• Intel MKL
• cmake

Python version:

• python >= 3.6
• pytorch >= 1.6
• numpy
• tqdm
• jupyter

C version: dataset_name n_words n_documents max_order n_epochs batch_size emb_dim lr kappa_init lam rdm

where:

• dataset_name - path to preprocessed dataset
• n_words - number of words in the dataset
• n_documents - number of documents in the dataset
• max_order - maximum order of tensor in the coupled decomposition (e.g, RDM-3 imply 4, because last tensor words x words x words x documents)
• n_epochs - number of epochs of SGD
• batch_size - batch size and number of negative samples (batch_size = n imply n - 1 negative samples)
• emb_dim - dimension of embedding and rank of tensor chain decomposition
• lr - learning rate
• kappa_init - initialization of kappa
• lam - strength of l2 regularization of kappa
• rdm - hyperparameter to choose between RDM (model with rotation constraints) and RDM-R (model without rotation constraints)

@inproceedings{vorona-etal-2021-documents,
    title = "Documents Representation via Generalized Coupled Tensor Chain with the Rotation Group constraint",
    author = "Vorona, Igor  and
      Phan, Anh-Huy  and
      Panchenko, Alexander  and
      Cichocki, Andrzej",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.146",
    doi = "10.18653/v1/2021.findings-acl.146",
    pages = "1674--1684",
}