Domain Adaptive Semi-supervised learning (DAS)

Codes and dataset for EMNLP2018 paper ‘‘Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification’’. (pdf)

Dataset & pretrained word embeddings

You can download the datasets (small-scale, large-scale, and amazon-benchmark) at [Download]. The zip file should be decompressed and put in the root directory.

Download the pretrained Glove vectors [glove.840B.300d.zip]. Decompress the zip file and put the txt file in the root directory.

Train & evaluation

You can find arguments and hyper-parameters defined in train_batch.py with default values.

Under code/, use the following command for training any source-target pair from small-scale dataset:

CUDA_VISIBLE_DEVICES="0" python train_batch.py \
--emb ../glove.840B.300d.txt \
--dataset $dataset \
--source $source \
--target $target \

where --emb is the path to the pre-trained word embeddings. $dataset in ['small_1', 'small_2'] denotes the experimental setting 1 and 2 respectively on the small-scale dataset. $source and $target are domains from the small-scale dataset, both in ['book', 'electronics', 'beauty', 'music']. All other hyper-parameters are left as their defaults.

To train on any source-target pair from the large-scale dataset, use:

CUDA_VISIBLE_DEVICES="0" python train_batch.py \
--emb ../glove.840B.300d.txt \
--dataset large \
--source $source \
--target $target \
-b 250 \
--weight-entropy 0.2 \
--weight-discrepancy 500 \

where $source and $target are domains from the large-scale dataset, both in ['imdb', 'yelp2014', 'cell_phone', 'baby']. The batch_size -b is set to 250. The weights of target entropy loss and discrepancy loss are set to 0.2 and 500 respectively. All other hyper-parameters are left as their defaults.

To train on any source-target pair from the amazon benchmark, use:

CUDA_VISIBLE_DEVICES="0" python train_batch.py \
--emb ../glove.840B.300d.txt \
--dataset amazon \
--source $source \
--target $target \
--n-class 2 \

where $source and $target are domains from the amazon benchmark, both in ['book', 'dvd', 'electronics', 'kitchen']. --n-class denoting the number of output classes is set to 2 as we only consider binary classification (positive or negative) on this dataset. All other hyper-parameters are left as their defaults.

During training, the model's performance will be evaluated on development set at the end of each epoch. Accuracy and macro-F1 score on test set are recorded at the epoch where the model achieves the best classification accuracy on development set.

About the adaptation results

You can find the numerical results in Appendix Table 3 and Table 4. The current version of code is improved in batch sampling for the large-scale dataset. By running this code, an average of 2% macro-F1 improvements can be observed across all source-target pairs on the larget-scale dataset compared to results in Table 4 (c). The results on the small-scale dataset and amazon benchmark are not affected.

Dependencies

The code was only tested under the environment below:

• Python 2.7
• Keras 2.1.2
• tensorflow 1.4.1
• numpy 1.13.3

Cite

If you use the code, please cite the following paper:

@InProceedings{he-EtAl:2018,
  author    = {He, Ruidan  and  Lee, Wee Sun  and  Ng, Hwee Tou  and  Dahlmeier, Daniel},
  title     = {Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  publisher = {Association for Computational Linguistics}
}