Contrast-Enhanced Semi-supervised Text Classification with Few Labels
Authors: Austin Cheng-Yun Tsai, Sheng-Ya Lin, Li-Chen Fu11394-11402
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform large-scale experiments on five benchmark datasets with merely 30 labeled data for training and validation datasets per class. Table 2: Performance (test accuracy(%)) comparison with baselines. |
| Researcher Affiliation | Academia | Department of Computer Science and Information Engineering, National Taiwan University {r08922086, r09944044, lichen}@ntu.edu.tw |
| Pseudocode | No | The paper describes its methods using prose and mathematical formulations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate CEST on five public datasets (Table 1), including IMDB (Maas et al. 2011), SST-2 (Socher et al. 2013), Elec (Mc Auley and Leskovec 2013) for sentiment analysis and DBpedia (Mendes, Jakob, and Bizer 2012), AG News (Zhang, Zhao, and Le Cun 2015) for topic classification. |
| Dataset Splits | Yes | We randomly select 30 labeled data per class with different random seeds for training and validation set and use the test set in original dataset. Table 1: Dataset statistics. |
| Hardware Specification | Yes | The results are averaged for three runs, with each run taking 3-8 hours on an NVIDIA RTX3090. |
| Software Dependencies | No | The paper mentions using "huggingface's BERT (bert-base-uncased)" but does not specify versions for software dependencies like Python, PyTorch, or the Hugging Face library itself. |
| Experiment Setup | Yes | We set the maximum token length in sentences to 256 and clip tokens exceeding the limit. The learning rate is fixed to 1e 5, and hyper-parameters are set to k = 2, τ = 10, λ = 0.75, |SU| = 2000, dim(Z) = 128. |