Disarm

Disarm is a RoBERTa based neural system for Argument Relation Classification (ARC). The model leverages discourse markers through adversarial training on the Discovery dataset. In particular, Disarm maps into a joint embedding space the argumentative units from the ARC task and sentences from the Discourse Marker Detection task (DMD). This result is achieved through the use of a Gradient Reversal Layer.

This repository holds the code of the paper "Argument Relation Classification through Discourse Markers and Adversarial Training"

Installation

Disarm was tested on Python 3.10.12 using a Google Colab V100 system. It is recommended to set up the model in a virtual environment.

python -m venv [NAME_OF_ENV]
source [NAME_OF_ENV]/bin/activate

pip install -r requirements.txt

How to run the experiments

Unzip data.zip. This will create a new directory data/ containing the datasets StudentEssay ("student_essay"), Debatepedia ("debate") and M-ARG ("m-arg"). In the following sections, substitute [NAME_OF_DATASET] with one of the names in brackets.

Run adversarial training on ARC/DMD with grid search

python main.py --dataset [NAME_OF_DATASET] --adversarial --grid_search

Run adversarial training on a specific combination of loss weights

python main.py --dataset [NAME_OF_DATASET] --adversarial --discovery_weight [DISCOVERY_VALUE] --adv_weight [ADV_WEIGHT]

with [DISCOVERY_VALUE] and [ADV_WEIGHT] being scalar values in [0,1].

To run the tests on Multi-Task Learning (MTL), set the --adv_weight to 0.

Run RoBERTa+ finetuning

python main.py --dataset [NAME_OF_DATASET]

Visualize the embeddings

python main.py --dataset [NAME_OF_DATASET] --visualize [NAME_OF_DATASET_TO_PLOT]

where [NAME_OF_DATASET_TO_PLOT] must be either [NAME_OF_DATASET] or discovery In order to visualize the embeddings, you must have trained a model on the dataset you want to plot. After training, the best model is saved in a file called [NAME_OF_DATASET]_model.pt. The --visualize flag forces the program to load the model, calculate the embeddings of the dataset and plot them.