Confronta i metodi

	Apprendimento per trasferimento con LSTM	Classificazione basata su BERT
Campo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	2018 (ULMFiT; concept since ~2010)	2019
Ideatore	Howard, J. & Ruder, S. (ULMFiT); general concept: Pan & Yang (2010)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Tipo	Transfer learning / Sequential model	Pre-trained language model with fine-tuning
Fonte seminale	Howard, J. & Ruder, S. (2018). Universal Language Model Fine-Tuning for Text Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 328–339.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
Alias	LSTM Transfer Learning, Pre-trained LSTM, LSTM Fine-Tuning, ULMFiT-style LSTM Transfer	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
Correlati	5	4
Sintesi	Transfer Learning with LSTM is a technique in which a Long Short-Term Memory network is first pre-trained on a large source corpus or task, and then its learned weights are transferred and fine-tuned on a smaller target task. This approach, popularized by ULMFiT (Howard & Ruder, 2018), allows LSTM-based models to reach strong performance even when labeled target data is scarce.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.