The repository for EMNLP 2020 accepted paper "MODE-LSTM: A Parameter-efficient Recurrent Network with Multi-Scale for Sentence Classification".
- python 2.7
- tensorflow 1.14.0
- keras 2.2.4
The folder data
contains the dataset SST5
for testing. As for other datasets, the IE
can be downloaded according to paper "Dynamic Compositional Neural Networks over Tree Structure", and the remaining datasets can refer to repository TextCNN.
Here, we present a case how to process SST5
. First, you should download the pretrain word embedding glove.840B.300d.txt
from glove, and place it under folder data
.
To process the raw data SST5
, run the command
python text_process.py
You can run the command
python main.py
When you run this command, please sure you have run the data preprocessing file text_process.py
.
@inproceedings{ma-etal-2020-mode,
title = "{MODE}-{LSTM}: A Parameter-efficient Recurrent Network with Multi-Scale for Sentence Classification",
author = "Ma, Qianli and
Lin, Zhenxi and
Yan, Jiangyue and
Chen, Zipeng and
Yu, Liuhong",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
publisher = "Association for Computational Linguistics",
pages = "6705--6715"
}