Mask and Infill: Applying Masked Language Model for Sentiment Transfer
Authors: Xing Wu, Tao Zhang, Liangjun Zang, Jizhong Han, Songlin Hu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model on two review datasets with quantitative, qualitative, and human evaluations. Experimental results demonstrate that our models improve state-of-the-art performance. |
| Researcher Affiliation | Academia | 1Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China 2School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China |
| Pseudocode | Yes | Algorithm 1 Implementation of Mask and Infill approach. |
| Open Source Code | No | The paper does not provide a direct link to its own source code or explicitly state that its code is released. The provided links are for baseline models or evaluation tools. |
| Open Datasets | Yes | We experiment our methods on two review datasets from [Li et al., 2018]: Yelp and Amazon [He and Mc Auley, 2016] |
| Dataset Splits | Yes | We experiment our methods on two review datasets from [Li et al., 2018]: Yelp and Amazon [He and Mc Auley, 2016], each of which is randomly split into training, validation and testing sets. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper mentions using 'pre-trained BERTbase' and 'a CNN-based classifier' but does not specify software dependencies with version numbers (e.g., PyTorch 1.x.x, TensorFlow 2.x.x). |
| Experiment Setup | Yes | The input size is kept compatible with original BERT and relevant hyperparameters can be found in [Devlin et al., 2018]. The pre-trained discriminator is a CNN-based classifier [Kim, 2014] with convolutional filters of size 3, 4, 5 and use Word Piece embeddings. The hyperparameters in Equation 10 and 11 are selected by a grid-search method using the validation set. We fine-tune BERT to AC-MLM for 10 epochs, and further train 6 epochs to apply discriminator constraint. |