@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",
editor = "Ostermann, Simon and
Zhang, Sheng and
Roth, Michael and
Clark, Peter",
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://aclanthology.org/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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<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.</abstract>
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%0 Conference Proceedings
%T Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction
%A Kiyomaru, Hirokazu
%A Omura, Kazumasa
%A Murawaki, Yugo
%A Kawahara, Daisuke
%A Kurohashi, Sadao
%Y Ostermann, Simon
%Y Zhang, Sheng
%Y Roth, Michael
%Y Clark, Peter
%S Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F kiyomaru-etal-2019-diversity
%X 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.
%R 10.18653/v1/D19-6014
%U https://aclanthology.org/D19-6014
%U https://doi.org/10.18653/v1/D19-6014
%P 113-122
Markdown (Informal)
[Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction](https://aclanthology.org/D19-6014) (Kiyomaru et al., 2019)
ACL