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The Tensorflow code for this ACL 2018 paper: "Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms"
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# SWEM (Simple Word-Embedding-based Models) This repository contains source code necessary to reproduce the results presented in the following paper: * [*Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms*](https://arxiv.org/abs/1805.09843) (ACL 2018) This project is maintained by [Dinghan Shen](https://sites.google.com/view/dinghanshen). Feel free to contact [email protected] for any relevant issues. ## Prerequisite: * CUDA, cudnn * Python 2.7 * Tensorflow (version >1.0). We used tensorflow 1.5. * Run: `pip install -r requirements.txt` to install requirements ## Data: * For convenience, we provide pre-processed versions for the following datasets: DBpedia, SNLI, Yahoo. Data are prepared in pickle format, and each `.p` file has the same fields in the same order: * `train_text`, `val_text`, `test_text`, `train_label`, `val_label`, `test_label`, `dictionary(wordtoix)`, `reverse dictionary(ixtoword)` * These `.p` files can be downloaded from the links below. After downloading, you can put them into a `data` folder: * Ontology classification: [DBpedia (591MB)](https://drive.google.com/open?id=1EBmMise0LQu0QpO7T4a32WMFuTxAb6T0) * Natural language inference: [SNLI (101MB)](https://drive.google.com/open?id=1M13UswHThZYt-ARrHg6sN7Dlel-d6BB3), [SNLI-word-embeddings (129MB)](https://drive.google.com/open?id=1qzClw-ZJzWZ4ce0eXAG4dDXYUNnmeX4f) * Topic categorization: [Yahoo (1.7GB)](https://drive.google.com/open?id=1Dorz_CWZkHHpojVS4K4YUEhhczVLQgRc) ## Run * Run: `python eval_dbpedia_emb.py` for ontology classification on the DBpedia dataset * Run: `python eval_snli_emb.py` for natural language inference on the SNLI dataset * Run: `python eval_yahoo_emb.py` for topic categorization on the Yahoo! Answer dataset * Options: options can be made by changing `option` class in any of the above three files: - `opt.emb_size`: number of word embedding dimensions. - `opt.drop_rate`: the keep rate of dropout layer. - `opt.lr`: learning rate. - `opt.batch_size`: number of batch size. - `opt.H_dis`: the dimension of last hidden layer. * On a K80 GPU machine, training roughly takes about 3 minutes each epoch and 5 epochs for Debpedia to converge, 50 seconds each epoch and 20 epochs for SNLI, and 4 minutes each epoch and 5 epochs for the Yahoo dataset. ## Subspace Training & Intrinsic Dimension To measure the [*intrinsic dimension*](https://eng.uber.com/intrinsic-dimension/) of word-embedding-based text classification tasks, we compare SWEM and CNNs via subspace training in Section 5.1 of the paper. Please follow the instructions in folder [`intrinsic_dimension`](./intrinsic_dimension) to reproduce the results. ## Citation Please cite our ACL paper in your publications if it helps your research: ```latex @inproceedings{Shen2018Baseline, title={Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms}, author={Shen, Dinghan and Wang, Guoyin and Wang, Wenlin and Renqiang Min, Martin and Su, Qinliang and Zhang, Yizhe and Li, Chunyuan and Henao, Ricardo and Carin, Lawrence}, booktitle={ACL}, year={2018} } ```
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matched fp:30685b15d26e2b04, topic:deep-learning, topic:tensorflow
matched fp:30685b15d26e2b04, topic:representation-learning
matched fp:30685b15d26e2b04, topic:natural-language-processing, readme:text classification