ALP: Data Augmentation Using Lexicalized PCFGs for Few-Shot Text Classification
Authors: Hazel H. Kim, Daecheol Woo, Seong Joon Oh, Jeong-Won Cha, Yo-Sub Han10894-10902
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on few-shot text classification tasks demonstrate that ALP enhances many state-of-the-art classification methods. In this section, we present experimental results on our contributions. |
| Researcher Affiliation | Collaboration | 1 Yonsei University, Seoul, Republic of Korea 2 NAVER AI Lab, 3 Changwon National University, Changwon, Republic of Korea |
| Pseudocode | No | The paper provides an overview of the ALP algorithm in Figure 2 and describes its stages, but it does not include a formal pseudocode block or algorithm steps formatted like code. |
| Open Source Code | No | The paper does not provide an explicit statement or a link indicating that the source code for the described methodology (ALP) is publicly available. |
| Open Datasets | Yes | Usual few-shot learning refers to the setup where k samples per class are available for training and other disjoint k samples per class are available for validation, where k is usually small (k-shot learning). Table 2: Comparison of data augmentation methods. We use the Self-Training (ST) semi-supervised learning setup with k-shot samples for both training and validation, where k {5, 10}. |
| Dataset Splits | Yes | Usual few-shot learning refers to the setup where k samples per class are available for training and other disjoint k samples per class are available for validation, where k is usually small (k-shot learning). Table 2: Comparison of data augmentation methods. We use the Self-Training (ST) semi-supervised learning setup with k-shot samples for both training and validation, where k {5, 10}. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper mentions software like Fair Seq, BERT-Base classifier, and various SSL approaches (ST, UST, Mix Text), but does not specify their version numbers. |
| Experiment Setup | Yes | We use the official code with the recommended insertion, deletion, and swap ratios the authors provided. ... We select German as intermediate languages for back-translation using Fair Seq and set 0.9 as the random sampling temperature. ... We use the default masked proportion and the pre-trained weights provided by the authors. Throughout the experiments we generate 200 samples for all augmentation methods, unless specified differently. We have conducted experiments with 5 random samplings of the labeled data, shuffling of data being presented to the models, and the weight initialization. |