Automatic Sentence Generation Using Generative Adversarial Neural Networks

  • This work aims to create a natural language generation (NLG) base for further development of systems for automatic examination questions generation and automatic summarization in Hochschule Bonn-Rhein-Sieg and Fraunhofer IAIS, respectively. Nowadays both tasks are very relevant. The first can significantly simplify the university teachers' work and the second to be of assistance for a faster retrieval of knowledge from an excessively large amount of information that people often work with. We focus on the search for an efficient and robust approach to the controlled NLG problem. Therefore, though the initial idea of the project was the usage of the generative adversarial neural networks (GANs), we switched our attention to more robust and easily-controllable autoencoders. Thus, in this work we implement an autoencoder for unsupervised discovery of latent space representations of text, and show the ability of the system to generate new sentences based on this latent space. Apart from that, we apply Gaussian mixture techniques in order to obtain meaningful text clusters and thereby try to create a tool that would allow us to generate sentences relevant to the semantics of the Gaussian clusters, e.g. positive or negative reviews or examination questions on certain topic. The developed system is tested on several datasets and compared to GANs' performance.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Availability
Metadaten
Document Type:Master's Thesis
Language:English
Pagenumber:xvi, 128
URL:https://nbn-resolving.org/urn:nbn:de:0011-n-5523753
Referee:Paul Plöger, Gerhard Kraetzschmar
Publisher:Fraunhofer Publica
Contributing Corporation:Bonn-Aachen International Center for Information Technology; Fraunhofer IAIS
Date of first publication:2019/07/25
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Theses:Fachbereich / Informatik
Entry in this database:2019/08/07