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		<rdfs:label><![CDATA[Event Representation with Sequential, Semi-Supervised Discrete Variables]]></rdfs:label>
		<pub:title><![CDATA[Event Representation with Sequential, Semi-Supervised Discrete Variables]]></pub:title>
		<pub:publishedOn rdf:datatype="&xsd;dateTime">2021-06-06T00:00:00-05:00</pub:publishedOn>
		<pub:abstract><![CDATA[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.]]></pub:abstract>
		<pub:note><![CDATA[<a href="https://arxiv.org/abs/2010.04361">https://arxiv.org/abs/2010.04361</a>
<img style="width:100%" src="https://ebiquity.umbc.edu/blogger/wp-content/uploads/2021/05/Screen-Shot-2021-05-25-at-8.39.55-AM.png">]]></pub:note>
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		<pub:booktitle><![CDATA[Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)]]></pub:booktitle>
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