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Argument Clustering

Accompanying repository of our ECIR2021 Paper "Diversity Aware Relevance Learning for Argument Search". Arxiv

Python 3.8 PyTorch License: MIT

We used the dataset of the ECIR2020 paper "A Framework for Argument Retrieval", which is available by request from the authors.

Install requirements:

pip install -U pip
pip install -U -r requirements.txt

Preprocessing:

You have to request the dataset from the authors and adjust the paths in settings.py

Step 1: Extract all claims, premises and the matching

PYTHONPATH=src:$PYTHONPATH python3 executables/preprocessing/read_json.py --input_dir=... --output_dir=output/

Step 2: Precompute bert-features for the claims, premises and claim-premise pairs (choices=['pair', 'claims', 'premises'])

PYTHONPATH=src:$PYTHONPATH python3 executables/preprocessing/generate_features.py --mode=...

Step 3: Generate negative claim-premise pairs either randomly or based on similarity

# Either
PYTHONPATH=src:$PYTHONPATH python3 executables/preprocessing/generate_negative_samples_nn.py
# or
PYTHONPATH=src:$PYTHONPATH python3 executables/preprocessing/generate_negative_samples.py

Step 4: Generate train-/test-/validation split

PYTHONPATH=src:$PYTHONPATH python3 executables/preprocessing/generate_sets.py

Execution:

PYTHONPATH=src:$PYTHONPATH python3 executables/evaluation/evaluate_baselines.py --force > output/output_energy.txt