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You May Not Need Attention
Ofir Press and Noah A. Smith
arXiv e-Print archive - 2018 via Local arXiv
Keywords: cs.CL

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Summary by CodyWild 2 years ago
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Not all research advances are made with state of the art models. Sometimes new methods are introduced that are slow, parameter-heavy or have some other deficiency. Such ideas are not meant to be introduced into production servers, they are meant to spark a discussion, which could then lead the research community to discover new ideas which will one day be used to improve state of the art models. -------------------------------------------------------------------------------------------- This paper does not try to present a state of the art model. This paper was written to question two commonly held beliefs- that separate encoding/decoding are necessary in NMT and that attention is a required component of all NMT models. If you look at the many NMT models published in the last two years, all of them contain at least one of these properties, and the vast majority contain both. -------------------------------------------------------------------------------------------- This paper is not claiming that we should just throw away all of that progress. We just want to show the research community that it is possible to build vastly different translation models that can still perform well. This specific model that we presented also has the advantage of being extremely simple. Usually new NMT papers introduce a new mechanism, they make an existing model more complex. Here we want to step forward by showing an NMT model that is simpler than almost anything else that came before it. -------------------------------------------------------------------------------------------- Yes, its not state of the art. Yes, it is trained using external alignment data. Yes, it requires special preprocessing. But it shatters two widely held beliefs. It also uses a constant amount of memory. And it works well on long sequences, which are known to be difficult for attention models. We firmly believe that these advantages far outweigh the disadvantages of our model. And *that* is why we posted this paper. We think the community should start thinking more about models that don’t use attention. Or models that have combined encoding/decoding. Or maybe just take the eagerness property from our model and apply it to an attention model. These research directions could lead to an improvement in performance of state of the art models on long sequences. Or they could be used to lower the memory requirements of simultaneous translation systems. Interesting methods aren’t found only in state of the art models.

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Summary by Ofir Press 2 years ago
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