Does NMT invert the encoder sequence?

[denisb411]

I read on some articles that inverting the input text words is a commom task on seq2seq encoder-decoder networks, but I can't see nmt model doing this. Is this task done on code?
Does this technique really improves the network's accuracy?

An example of where I saw this:
Hands-On Machine Learning with Scikit-Learn and TensorFlow By Aurélien Géron - Page 407

(please notify me if prints a page of this book is an illegal activity)

[oahziur]

We used to have an option for reverse the source sequence, see this change.

We usually use a Bi-Directional RNN encoder in the NMT, so reverse the source sentence doesn't help in this case. I think it can help if you are using an uni-directional RNN encoder for enfr or fren translation.

[nbro]

@oahziur Why doesn't it help to reverse the order of the words in the input sequence if you use a bi-directional encoder?

[denisb411]

Hello @nbro, it's because the bidirectional rnn generate outputs and states for both orders (normal sequence and inverted sequence), so it's the double of information extracted than an unidirectional rnn.
@oahziur Isn't valid an implementation of inverting the sentence order when using unidirectional encoders, added as a hparam? Edit: just checked the change. Ignore this.

[nbro]

@denisb411 It doesn't mean reversing the order wouldn't still help. It would be nice to have some tests.

[denisb411]

@nbro technically yes, because the inversion would make no difference as both orders are being computed. Surely there's some paper proving its improvement.

[lmthang]

Hi, since the introduction of attention mechanism and usages of biRNN, inversing the source sentence becomes less important. It also makes the code less clean and more complicated, so I removed it. Feel free to add it back in your code.
So close for now.