Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Self-Supervised Logic Induction for Explainable Fuzzy Temporal Commonsense Reasoning
Authors: Bibo Cai, Xiao Ding, Zhouhao Sun, Bing Qin, Ting Liu, Baojun wang, Lifeng Shang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on TIMEDIAL, a challenging dataset for temporal reasoning over dialog, show that our method, Logic Induction Enhanced Contextualized TEmporal Reasoning (LECTER), can yield great improvements over the traditional language model for temporal reasoning. |
| Researcher Affiliation | Collaboration | 1Research Center for Social Computing and Information Retrieval Harbin Institute of Technology, China 2Huawei Noah s Ark Lab |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper mentions 'The implementation is based on Pytorch' but does not provide a link or explicit statement about releasing the source code for LECTER. It links to a dataset: 'https://github.com/qywu/Dialog Corpus'. |
| Open Datasets | Yes | We evaluate the performance of our proposed LECTER model on the challenge dataset TIMEDIAL (Qin et al. 2021). and We leverage other large-scale publicly available corpus containing over 700MB of text1 to construct our selfsupervised training dataset... 1https://github.com/qywu/Dialog Corpus |
| Dataset Splits | Yes | After preprocessing, we obtain 97k/24k instances for training/validation. |
| Hardware Specification | Yes | The implementation is based on Pytorch and trained on a Tesla V100 GPU with Adam optimizer with 10 epochs. |
| Software Dependencies | No | The implementation is based on Pytorch, but no specific version number for PyTorch or other software dependencies is provided. |
| Experiment Setup | Yes | During the training, the batch size is set to 32. The combination weight λ in Eq.7 is set to 1. We search the learning rate with grid search in lr {5e 6, 1e 5, 5e 5} for the baseline and LECTER. The implementation is based on Pytorch and trained on a Tesla V100 GPU with Adam optimizer with 10 epochs. |