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The Dark Side of the Language: Pre-trained Transformers in the DarkNet

2022-01-14 16:04:09
Leonardo Ranaldi, Aria Nourbakhsh, Arianna Patrizi, Elena Sofia Ruzzetti, Dario Onorati, Francesca Fallucchi Fabio Massimo Zanzotto
arXiv_CL
       
Abstract

Pre-trained Transformers are challenging human performances in many natural language processing tasks. The gigantic datasets used for pre-training seem to be the key for their success on existing tasks. In this paper, we explore how a range of pre-trained natural language understanding models perform on truly novel and unexplored data, provided by classification tasks over a DarkNet corpus. Surprisingly, results show that syntactic and lexical neural networks largely outperform pre-trained Transformers. This seems to suggest that pre-trained Transformers have serious difficulties in adapting to radically novel texts.

Abstract (translated)

URL

https://arxiv.org/abs/2201.05613

PDF

https://arxiv.org/pdf/2201.05613.pdf

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