D-Separation for Causal Self-Explanation
Authors: Wei Liu, Jun Wang, Haozhao Wang, Ruixuan Li, Zhiying Deng, YuanKai Zhang, Yang Qiu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we demonstrate that MCD improves the F1 score by up to 13.7% compared to previous state-of-the-art MMI-based methods. Our code is available at: https://github.com/jugechengzi/Rationalization-MCD. 5 Experiments 5.1 Datasets and metrics 5.2 Baselines and implementation details 5.3 Results |
| Researcher Affiliation | Collaboration | Wei Liu1 Jun Wang2 Haozhao Wang1 Ruixuan Li1 Zhiying Deng1 Yuankai Zhang1 Yang Qiu1 1School of Computer Science and Technology, Huazhong University of Science and Technology 2i Wudao Tech 1{idc_lw, hz_wang, rxli, dengzhiyingdd, yuankai_zhang, anders}@hust.edu.cn 2jwang@iwudao.tech |
| Pseudocode | No | The paper includes architectural diagrams (Figure 3) and mentions PyTorch implementation in the appendix, but it does not contain a dedicated pseudocode or algorithm block. |
| Open Source Code | Yes | Our code is available at: https://github.com/jugechengzi/Rationalization-MCD. |
| Open Datasets | Yes | Datasets 1) Beer Advocate (Mc Auley et al., 2012) is a multi-aspect sentiment prediction dataset widely adopted in rationalization studies. 2) Hotel Reviews (Wang et al., 2010) is another multi-aspect sentiment classification dataset containing less feature correlation |
| Dataset Splits | No | The paper uses datasets and mentions training, but does not explicitly provide details about training/validation/test splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | All models are trained on a RTX3090 GPU. |
| Software Dependencies | No | The paper mentions software components like GloVe, GRUs, Gumbel-softmax, and Adam, but it does not provide specific version numbers for any of these or for the core programming environment (e.g., Python, PyTorch). |
| Experiment Setup | Yes | We set the sparsity to be similar to previous methods by adjusting the sparsity regularization term (i.e., s) in Equation 4. |