Better Fine-Tuning via Instance Weighting for Text Classification
Authors: Zhi Wang, Wei Bi, Yan Wang, Xiaojiang Liu7241-7248
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that IW-Fit can consistently improve the classification accuracy on the target domain. |
| Researcher Affiliation | Collaboration | 1Department of Control and Systems Engineering, Nanjing University, Nanjing, China 2Tencent AI Lab, Shenzhen, China |
| Pseudocode | Yes | Algorithm 1: Instance Weighting based Fine-tuning (IW-Fit) for text classification |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The source dataset from 2015 Amazon reviews (Zhang, Zhao, and Le Cun, 2015; Conneau et al., 2016) is mainly about products of online shopping. The target dataset of Yelp reviews is obtained from the latest 2018 Yelp Dataset Challenge |
| Dataset Splits | No | The paper specifies 10,000 training instances and 1,000 testing instances for the target dataset but does not explicitly mention a separate validation split or its size. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software components like LSTM and CNN but does not provide specific version numbers for any software libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | The hyperparameters are set as: vocabulary size = 30, 000, embedding size = 100, LSTM hidden size = 200, FC layer size = 200, dropout rate = 0.2, L2 regularization = 1e 4, batch size = 32, fine-tuning epochs = 50, burn-in epochs = 10, learning rate = 1e 3. |