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Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding

In this paper, we develop a model that integrates synthetic scanpath generation with a scanpath-augmented language model, eliminating the need for human gaze data. Since the model’s error gradient can be propagated throughout all parts of the model, the scanpath generator can be fine-tuned to downstream tasks. We find that the proposed model not only outperforms the underlying language model, but achieves a performance that is comparable to a language model augmented with real human gaze data.

Setup

Clone repository:

git clone https://github.com/aeye-lab/EMNLP-SyntheticScanpaths-NLU-PretrainedLM

Install dependencies:

pip install -r requirements.txt

Download precomputed sn_word_len mean and std (from CELER dataset) for Eyettention model feature normalization:

wget https://github.com/aeye-lab/EMNLP-SyntheticScanpaths-NLU-PretrainedLM/releases/download/v1.0/feature_norm_celer.pickle

Run Experiments

To reproduce the results in Section 3.2:

python run_ETSA_ours.py
python run_ETSA_bert.py
python run_ETSA_PLM_AS.py

To reproduce the results in Section 3.3:

python run_glue_ours_high_resource.py
python run_glue_ours_low_resource.py
python run_glue_bert_high_resource.py
python run_glue_bert_low_resource.py

To pre-train the Eyettention model (For more details see https://github.com/aeye-lab/Eyettention):

run_pretrain_eyettention_celer_position_prediction.py

Cite our work

If you use our code for your research, please consider citing our paper:

@inproceedings{deng-etal-2023-pre,
    title = "Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding",
    author = {Deng, Shuwen  and
      Prasse, Paul  and
      Reich, David  and
      Scheffer, Tobias  and
      J{\"a}ger, Lena},
    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.400",
    doi = "10.18653/v1/2023.emnlp-main.400",
    pages = "6500--6507",
}