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    Ofir Press Shares Excerpt From Review He Says Rejected RNN Paper From EMNLP 2016

    Ofir Press shared an excerpt from a review questioning justification for tying embeddings and adding a projection matrix despite perplexity gains.

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    1 Source, 25d ago, first seen 25d ago

    TLDR

    Ofir Press posted the first half of a review that he says rejected a paper on RNN language models from EMNLP 2016. The review states that tying input and output embeddings plus an extra projection matrix improved perplexity in most experiments. It criticizes the lack of justification for these modeling choices and asks whether gains would carry over to realistic tasks, noting many parameterizations can boost perplexity without clear insight.

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    1 Source, first seen 25d ago

    Combined views

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    1 Source, first seen 25d ago

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    1 Source

    @OfirPressHere's the first half of the review that rejected the paper from EMNLP 2016: In this paper, the authors improve the perplexity of RNN language models by (i) tying the input and output embeddings and (ii) adding an extra projection matrix between the RNN state and the output embedding. Whilst the measures do improve the perplexity in most of the experiments, there isn't any justification provided for these modelling changes. There's lots of different ways you could parameterise an RNN model, and some may give you better perplexity than others, but what does that actually tell you, and will the improvements carry through to a realistic task?

    1 Source

    @OfirPressHere's the first half of the review that rejected the paper from EMNLP 2016: In this paper, the authors improve the perplexity of RNN language models by (i) tying the input and output embeddings and (ii) adding an extra projection matrix between the RNN state and the output embedding. Whilst the measures do improve the perplexity in most of the experiments, there isn't any justification provided for these modelling changes. There's lots of different ways you could parameterise an RNN model, and some may give you better perplexity than others, but what does that actually tell you, and will the improvements carry through to a realistic task?