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TLM: Token-Level Masking for Transformers (EMNLP2023)

The public code.
In this paper, we propose a new regularization scheme based on token-level rather than structure-level to reduce overfitting. Specifically, we devise a novel Token-Level Masking (TLM) training strategy for Transformers to regularize the connections of self-attention, which consists of two masking techniques that are effective and easy to implement. The underlying idea is to manipulate the connections between tokens in the multi-head attention via masking, where the networks are forced to exploit partial neighbors’ information to produce a meaningful representation.

Note

we release the code => BERT-base (bert-base-cased) with TLM on RTE

  • if you do not use attention dropout, set attention_probs_dropout_prob=0
  • if you do not use tlm, set --use_tlm=0

1 Installation

git clone https://github.com/Young1993/tlm.git
cd tlm/
pip install -r requirements.txt

1.1 Training with TLM based on Bert

cd glue
sh bert-base_sh/rte_train_tlm.sh

1.2 Training without TLM based on Bert

cd glue sh bert-base_sh/rte_train.sh

🆕1.3 Training with TLM based on Bart

  1. First, you need to install the pre-trained lm fnlp/bart-base-chinese;
  2. Second, run pre_process.py in cged/utils/pre_process.py
  3. The code is not high efficient for training, I will update it soon.
cd cged
sh train_tlm.sh 0

Notice

If you plan to apply tlm to decoder-only architecture, you can reference to the code in ./transformers/models/bert/modeling_bert.py.

Todo

  • ✅ Run Bert-base with TLM.
  • ✅ Run Bart-base with TLM
  • To test QWen/LLama/sqlcoder with TLM.
  • To pull requests code into Transformers.

Citation

@inproceedings{wu-etal-2023-tlm,
    title = "{TLM}: Token-Level Masking for Transformers",
    author = "Wu, Yangjun  and
      Fang, Kebin  and
      Zhang, Dongxiang  and
      Wang, Han  and
      Zhang, Hao  and
      Chen, Gang",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.871",
    pages = "14099--14111"
}

TLM: Token-Level Masking for Transformers (Wu et al., EMNLP 2023)

If you have any questions, please feel free to talk with me (email: [email protected])