MARTA: Leveraging Human Rationales for Explainable Text Classification
Authors: Ines Arous, Ljiljana Dolamic, Jie Yang, Akansha Bhardwaj, Giuseppe Cuccu, Philippe Cudré-Mauroux5868-5876
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive validation on real-world datasets shows that our framework significantly improves the state of the art both in terms of classification explainability and accuracy. We conduct an extensive evaluation on two real-world datasets |
| Researcher Affiliation | Collaboration | 1University of Fribourg, Switzerland, 2armasuisse, Switzerland, 3Delft University of Technology, Netherlands |
| Pseudocode | Yes | Algorithm 1: Learning MARTA Parameters |
| Open Source Code | Yes | Source code and data are available at https://github.com/eXascaleInfolab/MARTA. |
| Open Datasets | Yes | We use two datasets for our experiments: Wiki Tech and Amazon1. Amazon is developed and published by Ram ırez et al. (2019). It contains 400 reviews with ground truth labels about reviews written about books ; this dataset is released with worker s rationales. Source code and data are available at https://github.com/eXascaleInfolab/MARTA. |
| Dataset Splits | Yes | We split the datasets into training, validation, and test sets. We use 50% of the data for training and the rest for validation and test with equal split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as CPU or GPU models, memory, or cloud computing instance types. |
| Software Dependencies | No | The paper mentions using "Sci BERT" and "ALBERT" as pre-trained language models, but it does not specify version numbers for these or any other software libraries, programming languages (e.g., Python), or frameworks (e.g., PyTorch, TensorFlow) used in the experiments. |
| Experiment Setup | No | While the paper describes the model architecture, data splits, and general learning process (variational inference), it does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or optimizer configurations, which are necessary for full reproducibility. |