 We examine their application to the stylistic task of authorship attribution , and show that they can substantially outperform both classification- and existing similarity-based approaches on datasets with large numbers of authors .
<meaning-changed>  We examine their application to the stylistic task of authorship attribution , and show that they can substantially outperform both classification- and existing similarity-based approaches on datasets with large numbers of authors .
 We examine their application to the stylistic task of authorship attribution on datasets with large numbers of authors, looking at multiple energy functions and neural network architectures, and show that they can substantially outperform both classification- and existing similarity-based approaches on datasets with large numbers of authors .