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Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction

Development Environment

  • Ubuntu 16.04
  • Python 3.6.5
  • chainer 5.4.0
  • cupy 5.4.0
  • gensim 3.8.0
  • scipy 1.3.0
  • nltk 3.4.5
  • pandas 0.25.0
  • progressbar2 3.42.0

Getting Started

Installation

$ pip install pipenv --user
$ pipenv install

Downloading Dataset

$ ./download.sh  # the resultant data size will be 3.5GB

Training

The configuration files used in the experiments are in ./config. To start training, run ./src/train.py with a configuration file. The results will be written into ./result

$ pipenv run python src/train.py "config/descript/descript-Seq2seq-batchsize.64-epoch.100-lr.0.001-n_layers.2-n_units.300"

Test

To evaluate trained models, run ./src/test.py with a glob pattern to grep result directories.

$ pipenv run python src/test.py "./result/descript/*"  # specify a pattern to glob result directories

To evaluate trained models leaned with different random seeds, run ./src/test.py with multiple glob patterns.

$ pipenv run python src/test.py "./result/descript-seed-0/*" "./result/descript-seed-1/*" "config/descript-seed-2/*"

Generation

To generate next events using a trained model, run ./src/generate_interactively.py with a glob pattern to grep result directories.

$ pipenv run python src/generate_interactively.py "./result/descript/*"

License

  • code: MIT License
  • data: GNU General Public License, version 2

Reference

@inproceedings{kiyomaru-etal-2019-diversity,
    title = "Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction",
    author = "Kiyomaru, Hirokazu  and
      Omura, Kazumasa  and
      Murawaki, Yugo  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6014",
    doi = "10.18653/v1/D19-6014",
    pages = "113--122",
    abstract = "Typical event sequences are an important class of commonsense knowledge. Formalizing the task as the generation of a next event conditioned on a current event, previous work in event prediction employs sequence-to-sequence (seq2seq) models. However, what can happen after a given event is usually diverse, a fact that can hardly be captured by deterministic models. In this paper, we propose to incorporate a conditional variational autoencoder (CVAE) into seq2seq for its ability to represent diverse next events as a probabilistic distribution. We further extend the CVAE-based seq2seq with a reconstruction mechanism to prevent the model from concentrating on highly typical events. To facilitate fair and systematic evaluation of the diversity-aware models, we also extend existing evaluation datasets by tying each current event to multiple next events. Experiments show that the CVAE-based models drastically outperform deterministic models in terms of precision and that the reconstruction mechanism improves the recall of CVAE-based models without sacrificing precision.",
}

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Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction (COIN2019)

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