Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/81989
Title: | DeepTingle |
Authors: | Khalifa, Ahmed Barros, Gabriella A. B. Togelius, Julian |
Keywords: | Text processing (Computer science) Artificial intelligence |
Issue Date: | 2017 |
Publisher: | Institute of Electrical and Electronics Engineers |
Citation: | Khalifa, A., Barros, G. A. B., & Togelius, J. (2017). DeepTingle. 2017 3rd IEEE International Conference on Computer and Communications, Chengdu. |
Abstract: | DeepTingle is a text prediction and classification system trained on the collected works of the renowned fantastic gay erotica author Chuck Tingle. Whereas the writing assistance tools you use everyday (in the form of predictive text, translation, grammar checking and so on) are trained on generic, purportedly “neutral” datasets, DeepTingle is trained on a very specific, internally consistent but externally arguably eccentric dataset. This allows us to foreground and confront the norms embedded in data-driven creativity and productivity assistance tools. As such tools effectively function as extensions of our cognition into technology, it is important to identify the norms they embed within themselves and, by extension, us. DeepTingle is realized as a web application based on LSTM networks and the GloVe word embedding, implemented in JavaScript with Keras-JS. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/81989 |
Appears in Collections: | Scholarly Works - InsDG |
Files in This Item:
File | Description | Size | Format | |
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DeepTingle_2017.pdf | 422.98 kB | Adobe PDF | View/Open |
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