Autoencoder: Difference between revisions

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Revision as of 22:42, 14 August 2023

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=== Machine translation ===

Autoencoders have been applied to [[machine translation]], which is usually referred to as [[neural machine translation]] (NMT).<ref>{{cite arXiv |eprint=1409.1259|last1=Cho|first1=Kyunghyun|author2=Bart van Merrienboer|last3=Bahdanau|first3=Dzmitry|last4=Bengio|first4=Yoshua|title=On the Properties of Neural Machine Translation: Encoder-Decoder Approaches|class=cs.CL|date=2014}}</ref><ref>{{cite arXiv |eprint=1409.3215|last1=Sutskever|first1=Ilya|last2=Vinyals|first2=Oriol|last3=Le|first3=Quoc V.|title=Sequence to Sequence Learning with Neural Networks|class=cs.CL|date=2014}}</ref> Unlike traditional autoencoders, the output does not match the input - it is in another language. In NMT, texts are treated as sequences to be encoded into the learning procedure, while on the decoder side sequences in the target language(s) are generated. [[Language]]-specific autoencoders incorporate further [[linguistic]] features into the learning procedure, such as Chinese decomposition features.<ref>{{cite arXiv |eprint=1805.01565|last1=Han|first1=Lifeng|last2=Kuang|first2=Shaohui|title=Incorporating Chinese Radicals into Neural Machine Translation: Deeper Than Character Level|class=cs.CL|date=2018}}</ref> Machine translation is rarely still done with autoencoders, butdue ratherto the availability of more effective [[Transformer (machine learning model)|transformer]] networks.

==See also==