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| Huomiomekanismi× | BERT-upotukset – kontekstisidonnaiset tekstiesitykset× | |
|---|---|---|
| Tieteenala≠ | Syväoppiminen | Tekstinlouhinta |
| Menetelmäperhe≠ | Machine learning | Process / pipeline |
| Syntyvuosi≠ | 2015 | 2019 |
| Kehittäjä≠ | Bahdanau, D.; Luong, M.T. | Devlin, Chang, Lee & Toutanova (Google AI) |
| Tyyppi≠ | Neural attention layer (encoder-decoder) | Contextual transformer text-representation method |
| Alkuperäislähde≠ | Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR. link ↗ | Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186. DOI ↗ |
| Rinnakkaisnimet≠ | Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention | contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri |
| Liittyvät≠ | 5 | 4 |
| Tiivistelmä≠ | 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. |
| ScholarGateAineisto ↗ |
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