Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation — mentioned by ndh2

Tue, 18 Jul 2017 06:36:52 GMT

Interesting, thanks! On https://www.tensorflow.org/tutorials/seq2seq I found a link to https://arxiv.org/abs/1406.1078, which says > $One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols.$ To me it sounds like they use an RNN to learn a hash function. Thanks for the NTM link, I'll check it out.

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Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation — mentioned by ogrisel

Thu, 15 Jan 2015 09:01:51 GMT

New neural machine translation architectures are experimenting with pairs of neural encoders / decoders, one pair for each language and a shared language independent vector space for the meaning of all words: http://arxiv.org/abs/1406.1078 http://arxiv.org/abs/1409.3215 http://arxiv.org/abs/1410.8206 So the total number of models is still linear with the number of languages. I do not know whether this new generation of translation models is leveraged by the google translation app though. Also pairs of languages for which their are big amount of parallel training data will still be favored.

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Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation · HackerPapers discussions

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation — mentioned by ndh2

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation — mentioned by ogrisel