# Machine Translation Weekly 14: Modeling Confidence in Sequence-to-Sequence Models

Neural machine translation is based on machine learning—we collect training data, pairs of parallel sentences which we hope represent how language is used in the two languages, and train models using the data. When the model is trained, the more the input resembles the sentences the model was trained on, the better we can expect the translation to be. If we were able to compute this similarity, we might be able to guess, how well the model can translate the sentence.

Current best systems are trained on tens or even hundreds of millions of sentence pairs. Obviously, comparing every single sentence you want to translate to so many sentences is not computationally feasible. This problem (and an elegant solution of it) is a topic of a recent paper from the Karlsruhe Institute of Technology called Modeling Confidence in Sequence-to-Sequence Models. The paper is based on a simple and clever trick that allows estimating this similarity. But before we get to the trick, let us follow (what I think was) the thinking of the authors.

If the input sentence is similar to sentences that were in the training data, the representations in the encoder and in the decoder should also be similar. This itself is not really helpful: computing the similarity still requires to go through millions of training sentences which is computationally expensive. The authors actually did that, burned a lot of electric power (warmed the planet a little bit) and proved that this really gives a good estimate of how good the translation will be. The question is now how to do it efficiently, i.e., how to compute the similarity with the training sentences without actually using them?

And indeed, there is a clever trick to do that. In the paper, they train an autoencoder for the hidden states. An autoencoder is a neural network that projects its inputs into a vector of a smaller dimension and tries to reconstruct the input from this intermediate representation. The intermediate representation is an information bottleneck because it simply does not have enough capacity to memorize the input. The network needs to learn how to compress its input to be able to reconstruct it. Because the network is trained to work well on average, frequent inputs get reconstructed better than inputs that appear only rarely. And voilà, this is exactly what we want: the reconstruction error can serve as an indirect estimate of how similar are the encoder and decoder states for the input sentence and for the training data.

The paper goes even further with this idea. When we have the hopefully well-reconstructed decoder states, we can plug them into the model instead of the original ones. We thus do not have to measure cosine or whatever similarity of the hidden states for which we do not have a straightforward interpretation anyway. We can simply try how the model output would change if we used the reconstructed states instead of the original ones. The outputs are words which got assigned the highest probability by the model. If the actual model output gets a low probability given the reconstructed hidden states, it means the original prediction was based on a hidden state that was not typical for training data and the translation is not likely to be of good quality.

Moreover, if we do word alignment (which they did in a quite clever way in the paper) between the source sentence and the model output, we can say what source words make the model more likely to fail. This feature can find use both in user interfaces and analysis of what the models do.

Isn’t it amazing? Instead of complaining that the hidden states of neural machine translation models are totally uninterpretable and that we cannot say anything about the expected translation quality, this paper shows that with a little wit and simple statistics, this can be nearly turned into an advantage.

**BibTeX Reference**

```
@misc{niehues2019modeling,
title={Modeling Confidence in Sequence-to-Sequence Models},
author={Jan Niehues and Ngoc-Quan Pham},
year={2019},
eprint={1910.01859},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```