A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
Transparent text quality assessment with convolutional neural networks
2017
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
We present a very simple model for text quality assessment based on a deep convolutional neural network, where the only supervision required is one corpus of usergenerated text of varying quality, and one contrasting text corpus of consistently high quality. Our model is able to provide local quality assessments in different parts of a text, which allows visual feedback about where potentially problematic parts of the text are located, as well as a way to evaluate which textual features are
doi:10.18653/v1/w17-5031
dblp:conf/bea/OstlingG17
fatcat:3pyjfyhvizbfbf32dqjy4g6qra