Publication
Title
Assessing the stylistic properties of neurally generated text in authorship attribution
Author
Abstract
Recent applications of neural language models have led to an increased interest in the automatic generation of natural language. However impressive, the evaluation of neurally generated text has so far remained rather informal and anecdotal. Here, we present an attempt at the systematic assessment of one aspect of the quality of neurally generated text. We focus on a specific aspect of neural language generation: its ability to reproduce authorial writing styles. Using established models for authorship attribution, we empirically assess the stylistic qualities of neurally generated text. In comparison to conventional language models, neural models generate fuzzier text, that is relatively harder to attribute correctly. Nevertheless, our results also suggest that neurally generated text offers more valuable perspectives for the augmentation of training data.
Language
English
Source (book)
Proceedings of the Workshop on Stylistic Variation, collocated with EMNLP 2017 (Copenhagen, September 8, 2017) / Association for Computational Linguistics
Publication
2017
DOI
10.18653/V1/W17-4914
Volume/pages
p. 116-125
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Project info
InterStylar: A Stylometric Approach to Intertextuality in 12th century Latin Literature.
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identifier
Creation 17.10.2017
Last edited 24.01.2022
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