RegionEmbedding

MXNet implementation of ICLR 2018 paper: A new method of region embedding for text classification.

Official implementation in TensorFlow.

0.Notes

• I implemented both Word-Context and Context-Word Region Embedding in the paper.
• Please see the original papar about the datasets and pre-pocessing.
• All the hyper-parameters I used are copied from the official implementation.

1.Requirements

• Python2 or Python3
• Mxnet 1.2.1

2.Results

Datasets	Accuracy(%) WordContext	Best Epoch WordContext	Accuracy(%) ContextWord	Best Epoch ContextWord	Running Time Per Epoch(mins)
Yahoo Answer	73.07(73.7)	2	73.42(73.4)	3	110
Amazon Polarity	95.27(95.1)	2	95.36(95.3)	3	247
Amazon Full	61.58(60.9)	2	61.59(60.8)	2	183
Ag news	92.96(92.8)	6	92.89(92.8)	8	2
DBPedia	98.91(98.9)	4	98.88(98.9)	3	23
Yelp Full	64.98(64.9)	3	64.94(64.5)	2	25

Note:

• The accuracy in brackets are results reported in the original paper.
• The running speed is much faster than the origin implementation in Tensorflow.
  The running time was tested on the model of context-word region embedding, which run roughly the same as the word-context region embedding.
• The code run on a Titan Xp GPU.