📝 Simultaneous Translation CZ → EN

Simul-Translation is a research-oriented toolkit for text-to-text Simultaneous Machine Translation (SimulMT) with support for dataset preparation, model training, fine-tuning, evaluation, and visualization. It includes modular components for alignment-based policies, local agreement strategies, and output analysis.

The main script main.py allows you to run model evaluation experiments with different configurations. It supports various command-line arguments to customize the behavior of the script.

The Data_preparation folder contains the create_dataset.py script for preparing our training and testing datasets.

The Finetuning folder contains the finetune_nmt_backbone.py script for fine-tuning the NMT backbone model on a dataset.

The AlignAtt folder contains the definitions of align att and local agreement policies and runs the model using them.

The Evaluation folder contains the scripts for the evaluation metrics, which can be used independently or by running the main script.

The Visualization folder contains scripts for parsing results, creating tables, and plotting results.

The AlignAttOutputs folder contains the output logs from the grid search over local agreement and align att parameters, parsed results, and final results.

🛠️ Technologies Used

---

🔧 Installation

Each module contains its own requirements.txt. To install dependencies for a module, run:

pip install -r <module>/requirements.txt

Example:

pip install -r Data_preparation/requirements.txt

Alternitavely, to install all dependencies for all modules, run:

pip install -r requirements.txt

---

⚙️ Command-line Arguments for main.py

Argument	Type	Default	Description
--dataset_path	str	./Data_preparation/cleaned_eval_dataset.jsonl	Path to the JSONL dataset file.
--local_agreement_length	int	0	Number of next tokens to agree with the previous theory.
--skip_l	int	0	Number of last positions in attention_frame_size to ignore.
--layers	int list	3, 4	Layer indices to use. The attention values are averaged over these layers.
--top_attentions	int	0	Top attentions to use (0 disables AlignAtt).
--output_file	str	results.jsonl	Output file for results.
--attention_frame_size	int	10	Excluded frame of last positions size.
--count_in	int	1	Required top_attentions within attention_frame_size for position to be bad.
--wait_for	int	0	Static wait time applied globally.
--wait_for_beginning	int	3	Wait time applied at the beginning.
--heads	int list	0 1 2 3 4 5	Attention heads to use. The attention values are averaged over these heads.
--device	str	cuda	Device (cuda or cpu).
--words_per_prefix	int	2	Words per prefix shown.
--forced_bos_token_text	str	None	Forced BOS token text.
--model_id	int	0	Model ID from predefined list.
--num_beams	int	2	Number of beams for beam search (multiple of 3 for diverse beam search).
--num_swaps	int	0	Number of word pairs to blindly swap.
--src_key	str	source	Source key in dataset.
--tgt_key	str	target	Target key in dataset.
--verbose	flag	True	Enable verbose output.
--experiment_type	str	none	Experiment type (simple, alignatt). The simple option does a grid search over num_beams and wait_for_beginning. The alignatt option does a grid search over layers and attention_frame_size.

📜 Usage Example

python main.py \
--dataset_path ./Data_preparation/cleaned_eval_dataset.jsonl \
--experiment_type alignatt \
--layers 3 \
--heads 0 1 2 3 4 5 \
--model_id 0 \
--device cuda \
--output_file results.jsonl

---

📂 Project Structure

Simul-Translation/
├── .gitignore
├── main.py
├── README.md
├── report.pdf
├── requirements.txt
├── slides.pdf
├── AlignAtt/
│ ├── README.md
│ ├── alignatt.py
│ ├── analyze_dataset.py
│ ├── get_data.py
│ ├── local_agreement.py
│ ├── requirements.txt
│ └── translate.py
├── AlignAttOutputs/
│ ├── output_logs_grid_search_local_agreement/
│ ├── parsed/
│ └── results/
├── Data_preparation/
│ ├── README.md
│ ├── cleaned_eval_dataset.jsonl
│ ├── create_dataset.py
│ ├── iwslt2024_cs_devset.json
│ └── requirements.txt
├── Evaluation/
│ ├── README.md
│ ├── requirements.txt
│ └── simueval.py
├── Finetuning/
│ ├── README.md
│ ├── finetune_nmt_backbone.py
│ ├── finetuning.log
│ └── requirements.txt
└── Visualization/
├── RESULTS.md
├── RESULTS_OVERALL.md
├── best.json
├── create_tables.py
├── parse_results.py
└── plot_results.py