Transfer Learning for Sequences via Learning to Collocate
Authors: Wanyun Cui, Guangyu Zheng, Zhiqiang Shen, Sihang Jiang, Wei Wang
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted extensive experiments on both sequence labeling tasks (POS tagging, NER) and sentence classification (sentiment analysis). |
| Researcher Affiliation | Academia | Shanghai University of Finance and Economics Shanghai Key Laboratory of Data Science, Fudan University Shanghai Key Laboratory of Intelligent Information Processing, Fudan University |
| Pseudocode | No | The paper describes the architecture and provides mathematical formulas, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code related to the methodology. |
| Open Datasets | Yes | We use the Amazon review dataset (Blitzer et al., 2007), which has been widely used for cross-domain sentence classification. |
| Dataset Splits | Yes | We use the training data and development data from both domains for training and validating. And we use the testing data of the target domain for testing. |
| Hardware Specification | Yes | All the experiments run over a computer with Intel Core i7 4.0GHz CPU, 32GB RAM, and a Ge Force GTX 1080 Ti GPU. |
| Software Dependencies | No | The paper mentions optimizers (Adam, Adagrad) and specific embedding vectors (GloVe) but does not provide version numbers for any key software components or libraries (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | We use 100d Glo Ve vectors (Pennington et al., 2014) as the initialization for ART and all its ablations. The dimension of each LSTM is set to 100. We use the Adam (Kingma & Ba, 2015) optimizer. We use a dropout probability of 0.5 on the max pooling layer. |