The Dark Side of the Language: Pre-trained Transformers in the DarkNet
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866916234658840576 |
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| author | Ranaldi, Leonardo Nourbakhsh, Aria Patrizi, Arianna Ruzzetti, Elena Sofia Onorati, Dario Fallucchi, Francesca Zanzotto, Fabio Massimo |
| author_facet | Ranaldi, Leonardo Nourbakhsh, Aria Patrizi, Arianna Ruzzetti, Elena Sofia Onorati, Dario Fallucchi, Francesca Zanzotto, Fabio Massimo |
| contents | Pre-trained Transformers are challenging human performances in many NLP tasks. The massive datasets used for pre-training seem to be the key to their success on existing tasks. In this paper, we explore how a range of pre-trained Natural Language Understanding models perform on definitely unseen sentences provided by classification tasks over a DarkNet corpus. Surprisingly, results show that syntactic and lexical neural networks perform on par with pre-trained Transformers even after fine-tuning. Only after what we call extreme domain adaptation, that is, retraining with the masked language model task on all the novel corpus, pre-trained Transformers reach their standard high results. This suggests that huge pre-training corpora may give Transformers unexpected help since they are exposed to many of the possible sentences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_05613 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | The Dark Side of the Language: Pre-trained Transformers in the DarkNet Ranaldi, Leonardo Nourbakhsh, Aria Patrizi, Arianna Ruzzetti, Elena Sofia Onorati, Dario Fallucchi, Francesca Zanzotto, Fabio Massimo Computation and Language Machine Learning Pre-trained Transformers are challenging human performances in many NLP tasks. The massive datasets used for pre-training seem to be the key to their success on existing tasks. In this paper, we explore how a range of pre-trained Natural Language Understanding models perform on definitely unseen sentences provided by classification tasks over a DarkNet corpus. Surprisingly, results show that syntactic and lexical neural networks perform on par with pre-trained Transformers even after fine-tuning. Only after what we call extreme domain adaptation, that is, retraining with the masked language model task on all the novel corpus, pre-trained Transformers reach their standard high results. This suggests that huge pre-training corpora may give Transformers unexpected help since they are exposed to many of the possible sentences. |
| title | The Dark Side of the Language: Pre-trained Transformers in the DarkNet |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2201.05613 |