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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:

This project is maintained by Dinghan Shen. Feel free to contact dinghan.shen@duke.edu for any relevant issues.

Prerequisite:

Data:

Run

Subspace Training & Intrinsic Dimension

To measure the 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 to reproduce the results.

Citation

Please cite our ACL paper in your publications if it helps your research:

@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} 
}