ExHiRD-DKG

The source code for the ACL 2020 paper Exclusive Hierarchical Decoding for Deep Keyphrase Generation

Citation
Dependencies
Get the processed train/val/test datasets
Process training and validation data to ONMT style
Training
Testing
Evaluate the predictions
Download our raw final predictions

Citation

You can cite our paper by:

@article{DBLP:journals/corr/abs-2004-08511,
  author    = {Wang Chen and
               Hou Pong Chan and
               Piji Li and
               Irwin King},
  title     = {Exclusive Hierarchical Decoding for Deep Keyphrase Generation},
  journal   = {CoRR},
  volume    = {abs/2004.08511},
  year      = {2020},
  url       = {https://arxiv.org/abs/2004.08511},
  archivePrefix = {arXiv},
  eprint    = {2004.08511},
  timestamp = {Wed, 22 Apr 2020 12:57:53 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2004-08511.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Dependencies

python 3.6.8
pytorch 1.0 (CUDA9.0)
torchtext 0.4.0

The full dependencies are listed in Requirements.txt.

Get the processed train/val/test datasets

Download the processed testing datasets. Download the processed train_valid_dataset.

The words in the file name have the following meanings:

PbfA_ordered: the present keyphrases are put in front of the absent keyphrases. The present keyphrases are sorted by their positions of the first appearance.
addBiStokens: One start token is added to each present keyphrase and The other start token is added to each absent keyphrase.
addSemicolon: For each keyphrase, we add ; as the ending token.
RmStemDups: The duplicated keyphrases (after stemming) is removed.
RmKeysAllUnk: After building the vocabulary, we remove the absent keyphrases in which every token is in neither the vocabulary nor the source input.

Process training and validation data to ONMT style

Create a new folder data under the home folder (i.e., ExHiRD-DKG), download the processed train_valid_dataset into data, unzip the .zip file. Run the following command lines to process the data into ONMT style:

cd sh/preprocess/
sh preprocess_kp20k_seqE_HRD_PbfA_ordered_addBiSTokens_addSemicolon_RmStemDups_RmKeysAllUnk.sh

Next, download the vocabulary file into the onmt-processed data folder: data/train_valid_dataset/onmt_processed_data/with_copy_seqE_HRD_PbfA_ordered_addBiSTokens_addSemicolon_RmStemDups_RmKeysAllUnk. Then, the processing is finished. The given "vocab.pt" is the built vocab before "RmKeysAllUnk". We performed "RmKeysAllUnk" based on the given vocab and chose the given vocab as the final vocab. If we generate the vocab after "RmKeysAllUnk", the "RmKeysAllUnk" may be meaningless since some keyphrases may become all unks based on the generated new vocab.

Note: To run all bash files smoothly, you may need to specify the correct home_dir (i.e., the absolute path to ExHiRD-DKG) and replace the python path to yours.

Training

Train an ExHiRD-h model:

cd sh/ExHiRD/
sh ExHiRD_h_seed3435_PbfA_ordered_addBiSTokens_addSemicolon_RmStemDups_RmKeysAllUnk_train.sh

Train an ExHiRD-s model:

cd sh/ExHiRD/
sh ExHiRD_s_seed3435_PbfA_ordered_addBiSTokens_addSemicolon_RmStemDups_RmKeysAllUnk_train.sh

You may need to set CUDA_VISIBLE_DEVICES in these bash files to run them smoothly.

Testing

Test a trained ExHiRD-h model:

cd sh/ExHiRD/
sh ExHiRD_h_seed3435_history1_4_PbfA_ordered_addBiSTokens_addSemicolon_RmStemDups_RmKeysAllUnk_translate.sh

Test a trained ExHiRD-s model:

cd sh/ExHiRD/
sh ExHiRD_s_seed3435_PbfA_ordered_addBiSTokens_addSemicolon_RmStemDups_RmKeysAllUnk_translate.sh

You may need to set CUDA_VISIBLE_DEVICES in these bash files to run them smoothly. The evaluation of the predictions is also integrated in this step and the evaluation results are stored in the corresponding log files.

Evaluate the predictions

After specifying the corret path of the predictions and testing datasets in the sh/evaluation/evaluate_ExHiRD_h.sh and sh/evaluation/evaluate_ExHiRD_h.sh, you can run the following command lines to evaluate the predictions of ExHiRD-h and ExHiRD-s:

cd sh/evaluation/
sh evaluate_ExHiRD_h.sh
sh evaluate_ExHiRD_s.sh

All the post-processing steps (including removing duplicated predictions, restricting the maximum number of single-word predictions (if set), and filtering predictions which contain dot, comma, or unk token.) and evaluation metrics are integrated in evaluation_utils.py. The standard Macro-averaged F1@5 (i.e. Macro std_F1@5) and F1@M (i.e. Macro std_F1@M) are reported in the paper.

Download our raw final predictions

You can download our raw final predictions of our ExHiRD-h and ExHiRD-s methods for the four testing datasets. In the folder, historyK means the window size of exclusive search is set as K. The prediction post-processing is integrated in evaluation_utils.py including removing duplicated predictions, restricting the maximum number of single-word predictions (if set), and filtering predictions which contain dot, comma, or unk token.

Chen-Wang-CUHK/ExHiRD-DKG