Shah, H;
Zheng, B;
Barber, D;
(2018)
Generating Sentences Using a Dynamic Canvas.
In:
Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18).
AAAI Association for the Advancement of Artificial Intelligence
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Abstract
We introduce the Attentive Unsupervised Text (W)riter (AUTR), which is a word level generative model for natural language. It uses a recurrent neural network with a dynamic attention and canvas memory mechanism to iteratively construct sentences. By viewing the state of the memory at intermediate stages and where the model is placing its attention, we gain insight into how it constructs sentences. We demonstrate that AUTR learns a meaningful latent representation for each sentence, and achieves competitive log-likelihood lower bounds whilst being computationally efficient. It is effective at generating and reconstructing sentences, as well as imputing missing words.
| Type: | Proceedings paper |
|---|---|
| Title: | Generating Sentences Using a Dynamic Canvas |
| Event: | Thirty-Second AAAI Conference on Artificial Intelligence, 2 – 7 February 2018, New Orleans, Louisiana, USA |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.aaai.org/ocs/index.php/AAAI/AAAI18/pap... |
| Language: | English |
| Additional information: | Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
| Keywords: | NLP; bayesian; variational; generative; sentences |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10059972 |
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