পদ্ধতির তুলনা করুন

	অ্যাটেনশন মেকানিজম	BERT এমবেডিং
ক্ষেত্র	গভীর শিখন	টেক্সট খনন
পরিবার	Machine learning	Process / pipeline
উদ্ভবের বছর	2015	2019
প্রবর্তক	Bahdanau, D.; Luong, M.T.	Devlin, Chang, Lee & Toutanova (Google AI)
ধরন	Neural attention layer (encoder-decoder)	Contextual transformer text-representation method
মৌলিক উৎস	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.
অপর নাম	Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri
সম্পর্কিত	5	4
সারসংক্ষেপ	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.