Master Thesis by Iuliia Mozhina, University of Potsdam, Institute of Computer Science (2024)
This work is based on the original Eyettention model introduced by Deng, et al (2023): https://arxiv.org/abs/2304.10784. Eyettention 2.0 is the first attention-based dual-sequence model capable of simultaneously predicting fixation locations, durations, and within-word landing positions.
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Beijing Sentence Corpus (BSC) The BSC dataset can be found in the /Data/beijing-sentence-corpus directory of this repository.
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CELER Instructions on how to download the CELER dataset can be found in the official repo of the dataset: https://github.com/berzak/celer .
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Zurich Cognitive Language Processing Corpus (ZuCo) The pre-processed ZuCo files can be found in the /Data/zuco directory of this repository. The original dataset can be found under https://osf.io/uxamg/ .
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Zurich Cognitive Language Processing Corpus 2.0 (ZuCo 2.0) The pre-processed ZuCo 2.0 files can be found in the /Data/zuco2 directory of this repository. The original dataset can be found under https://osf.io/2urht/ .
git clone [email protected]:iuliia-mozhina/eyettention-v2.0.git
pip install -r requirements.txt
- New Sentence split & New Reader split
Eyettention_BSC.ipynb
- New Sentence / New Reader split
Eyettention_BSC_NRS.ipynb
- Eyettention Reader 2.0
Eyettention_Reader_BSC.ipynb
- New Sentence split & New Reader split
Eyettention_CELER.ipynb
- New Sentence / New Reader split
Eyettention_CELER_NRS.ipynb
- Eyettention Reader 2.0
Eyettention_Reader_CELER.ipynb
- Train & test on Zuco
Eyettention_Zuco_training.ipynb
- Pre-train on CELER, fine-tune on Zuco
Eyettention_Zuco_finetuning.ipynb