@inproceedings{rezaee-ferraro-2021-event,
title = "Event Representation with Sequential, Semi-Supervised Discrete Variables",
author = "Rezaee, Mehdi and
Ferraro, Francis",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.374",
doi = "10.18653/v1/2021.naacl-main.374",
pages = "4701--4716",
abstract = "Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. We construct a sequential neural variational autoencoder, which uses Gumbel-Softmax reparametrization within a carefully defined encoder, to allow for successful backpropagation during training. The core idea is to allow semi-supervised external discrete knowledge to guide, but not restrict, the variational latent parameters during training. Our experiments indicate that our approach not only outperforms multiple baselines and the state-of-the-art in narrative script induction, but also converges more quickly.",
}
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%0 Conference Proceedings
%T Event Representation with Sequential, Semi-Supervised Discrete Variables
%A Rezaee, Mehdi
%A Ferraro, Francis
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F rezaee-ferraro-2021-event
%X Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. We construct a sequential neural variational autoencoder, which uses Gumbel-Softmax reparametrization within a carefully defined encoder, to allow for successful backpropagation during training. The core idea is to allow semi-supervised external discrete knowledge to guide, but not restrict, the variational latent parameters during training. Our experiments indicate that our approach not only outperforms multiple baselines and the state-of-the-art in narrative script induction, but also converges more quickly.
%R 10.18653/v1/2021.naacl-main.374
%U https://aclanthology.org/2021.naacl-main.374
%U https://doi.org/10.18653/v1/2021.naacl-main.374
%P 4701-4716
Markdown (Informal)
[Event Representation with Sequential, Semi-Supervised Discrete Variables](https://aclanthology.org/2021.naacl-main.374) (Rezaee & Ferraro, NAACL 2021)
ACL