Very neat! I will add this to https://github.com/Kaleidophon/experimental-standards-deep-learning-research :-) Maybe you can also add citation info in case people want to refer to the package in their publication?

>Why go with BLEU though ? OP didn't particularly mention optimizing sequence level metrics.

From OPs post above:

>is this possible at all ? I think if we can backprop through beam sampling, we can directly optimise for bleu ?

You can backpropagate through samples of a categorical distribution using Gumbel softmax, and as far as i remember you can apply a reparameterization trick for all distributions of the exponential family.

I think what you are looking for is the Gumbel-softmax trick, which is basically differentiable sampling for categorical distributions. But in your case the problem will be that BLEU is not differentiable, and often in MT you find that when you try to directly optimize for some translation quality metric, the actual quality as assessed by human judges decreases.

I find poster sessions much more educational than most plenary presentations, since you can interact with the presenters.

If you would like to connect to companies, talk to the recruiters at the booths as early as possible (you still have a chance to get swag and potentially an invitation to the socials).

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The paper reviewing process is very noisy. There a decent chance your paper will get rejected. Don't take it too much to heart! It does not mean that your paper is bad, just that the process has flaws. Also: You are not the number of accepted papers in your PhD, and often quality beats quantity.

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Lastly: Talk to people! Message people in advance you might like to connect with - conferences are big these days, you rarely just run into someone that you are looking for. Also, chatting with PhD can get you some perspective (e.g. showing that the grass isn't always greener on the other side).

I am sorry about your own insecurities