Event Representation with Sequential, Semi-Supervised Discrete Variables
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Date
2021-06-06
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Citation of Original Publication
Rezaee, Mehdi; Ferraro, Francis; Event Representation with Sequential, Semi-Supervised Discrete Variables; Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 6 June, 2021; https://aclanthology.org/2021.naacl-main.374/
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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 that uses a carefully defined encoder, and Gumbel-Softmax reparametrization, to allow for successful backpropagation during training. We show that our approach outperforms multiple baselines and the state-of-the-art in narrative script induction on multiple event modeling tasks. We demonstrate that our approach converges more quickly.