NeuralREG: An end-to-end approach to referring expression generation

ACL 2018

NeuralREG: An end-to-end approach to referring expression generation

Jan 28, 2021
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Abstract: Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on features such as salience and grammatical function. In this paper, we present a new approach (NeuralREG), relying on deep neural networks, which makes decisions about form and content in one go without explicit feature extraction. Using a delexicalized version of the WebNLG corpus, we show that the neural model substantially improves over two strong baselines. Authors: Thiago Castro Ferreira, Diego Moussallem, Ákos Kádár, Sander Wubben, Emiel Krahmer (Tilburg University, University of Leipzig, University of Paderborn)

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