Fallacious Argument Classification in Political Debates
Authors: Pierpaolo Goffredo, Shohreh Haddadan, Vorakit Vorakitphan, Elena Cabrio, Serena Villata
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results show the important role played by argument components and relations in this task. We propose a new transformer-based model architecture, fine-tuned on argumentation features, and we address an extensive evaluation obtaining very promising results. |
| Researcher Affiliation | Academia | 1Universit e Cˆote d Azur, CNRS, Inria, I3S, France 2University of Luxembourg, Luxembourg |
| Pseudocode | No | The paper includes a figure illustrating the pipeline for fallacious argument classification (Figure 1), but it does not provide any pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | 4https://github.com/pierpaologoffredo/IJCAI2022 |
| Open Datasets | Yes | To investigate fallacious arguments in political debates, we extend the annotations of the Elec Deb60To16 dataset [Haddadan et al., 2019], that collects televised debates of the presidential election campaigns in the U.S. from 1960 to 2016. 31 debates of the Elec Deb60To16 dataset are fully annotated with fallacies4. 4https://github.com/pierpaologoffredo/IJCAI2022 |
| Dataset Splits | No | The paper only specifies an '80% for training and the remaining 20% for testing' split, without explicitly mentioning a separate validation set. |
| Hardware Specification | No | The paper discusses software models and parameters but does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for experiments. |
| Software Dependencies | Yes | The implementation is based on the huggingface transformer5 using Py Torch (version 1.7.0). |
| Experiment Setup | Yes | The selected learning rate is 5e-5, dropout 0.1 and batch size 1 for Transformer-XL, and 8 for the rest. All transformer models apply Adam optimizer, dropout 0.1, and Cross Entropy as a loss function. |