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Hey HN, I'm author of the article. If there's anything confusing, I'd be happy to help :) The GNMT architecture is likely to keep popping up in Google's papers

by Smerity 10y ago

Hey HN, I'm author of the article. If there's anything confusing, I'd be happy to help :) The GNMT architecture is likely to keep popping up in Google's papers due to the amount of engineering that's been dedicated to make it scale well. Other than chewing up massive machine translation datasets, from single to multiple language pairs, it's also been trained over the entirety of Reddit for a conversational model system. Just this week a new paper[1] was released using the GNMT architecture which teaches a machine translation system how to translate between language pairs it has never been taught before by using bridging from other language pair knowledge. It's all pretty fascinating stuff :) [1]: https://arxiv.org/abs/1611.04558 https://arxiv.org/abs/1611.04558

zackmorris 10y ago
Great article, thank you for getting to the crux of the paper. Now that you have a base case working: * Could you convert the parameters that govern the creation and interconnect of each layer into lisp and feed the whole thing into a genetic algorithm to evolve even better translators? Using GAs to evolve NNs was the new hotness in the late 90s and I don't know what ever happened with it. It would take on the order of 10,000 times more computation to simulate all the generations, but hey, it's Google. * How hard would it be to generalize the GNMT for solving other types of problems where the output translation is the solution to a problem or what to do next? While reading the article, it occurred to me that the hidden layer S could be thought of as the meaning of the input data. If S is smaller than the input data, then you could treat it like a word feeding into another GNMT to process paragraphs, chapters, etc. I guess this would hinge on whether sentences have any relation to one another. In any case, would love to hear your thoughts on something like that. I'm just an armchair hobbyist toiling away my career building CRUD apps, but I really feel that if we can get beyond the learning curve (no pun intended) and get to basic building blocks like this, then engineers will run with it and scale it very quickly to a passable implementation of hard AI.

by Smerity 10y ago

zackmorris 10y ago

Great article, thank you for getting to the crux of the paper. Now that you have a base case working: * Could you convert the parameters that govern the creation and interconnect of each layer into lisp and feed the whole thing into a genetic algorithm to evolve even better translators? Using GAs to evolve NNs was the new hotness in the late 90s and I don't know what ever happened with it. It would take on the order of 10,000 times more computation to simulate all the generations, but hey, it's Google. * How hard would it be to generalize the GNMT for solving other types of problems where the output translation is the solution to a problem or what to do next? While reading the article, it occurred to me that the hidden layer S could be thought of as the meaning of the input data. If S is smaller than the input data, then you could treat it like a word feeding into another GNMT to process paragraphs, chapters, etc. I guess this would hinge on whether sentences have any relation to one another. In any case, would love to hear your thoughts on something like that. I'm just an armchair hobbyist toiling away my career building CRUD apps, but I really feel that if we can get beyond the learning curve (no pun intended) and get to basic building blocks like this, then engineers will run with it and scale it very quickly to a passable implementation of hard AI.