Comparar métodos

	Mecanismo de atención	BERT Embeddings
Campo	Aprendizaje profundo	Minería de texto
Familia	Machine learning	Process / pipeline
Año de origen	2015	2019
Autor original	Bahdanau, D.; Luong, M.T.	Devlin, Chang, Lee & Toutanova (Google AI)
Tipo	Neural attention layer (encoder-decoder)	Contextual transformer text-representation method
Fuente seminal	Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.
Alias	Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri
Relacionados	5	4
Resumen	The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.