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Topic-word-constrained sentence generation with variational autoencoder
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DOI：10.1016/j.patrec.2022.06.016.png)
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Abstract
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We propose a topic-word-constrained sentence-generation model with a variational autoencoder and convolutional neural network. It can generate sentences conditioned on a given topic distribution and a certain word. Unlike the vanilla variational autoencoder that assumes a standard Gaussian prior for the latent code, our model specifies the prior for the topic latent code as multiple Gaussian distributions, where each Gaussian distribution corresponds to a topic vector parameterized by a convolutional neural topic model. For word constraints, the decoder in the variational autoencoder generates sentences back-ward and forward starting from a given word. The topic latent space is arranged by the similarity of topic vectors, and the topic latent code restricts the sentence latent code through a loss term, through which expanded semantically meaningful latent spaces can be learned and provide topic guidance while gener-ating sentences. Experimental results show that our model can generate coherent and diverse sentences related to given topics and words, while also avoiding the Kullback-Leibler divergence collapse problem. Moreover, it outperforms alternative approaches in terms of sentence reconstruction, latent space prop-erty and the quality, diversity, and topic controllability of generated sentences.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Natural language generation
Variational autoencoder
Constrained sentence generation
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