Semi-supervised Text Style Transfer: Cross Projection in Latent Space

Supervised and unsupervised style transfer are often regarded as two independent paradigms, this paper proposed a semi-supervised method to bridge the gap between them. They train 2 S2S models in each style then learn 2 project functions between the latent space of the 2 encoders. The S2S models are denoising auto-encoders which require no parallel corpus. Once encoders are trained in each style, their hidden states are projected into the latent space of their counterparts under two criteria:

  1. For parallel sentence pairs (a, b) , the Euclidean distance between the original representation of b and projected hidden states of a in encoder B should be small.

  2. The probability of reproducing b giving projected a should be large.

To exploit non-parallel corpora, they propose a cycle projection method inspired by back-translation in machine translation, which is similar to the two criteria above.


  1. For sentence x from either domain, the Euclidean distance between the original representation and the hidden states of it being projected to the other domain then projected back should be small.

  2. Using the hidden states projected back, the probability of re-constructing x should be large.

These ideas are amazingly simple, but they turn out to be quite effective. The only weakness of this paper is that its writing is a little bit confusing. They could have presented a little more intuition before throwing out lots of equations.

Overall Recommendation

  • 5: Transformative: This paper is likely to change our field. It should be considered for a best paper award.
  • 4.5: Exciting: It changed my thinking on this topic. I would fight for it to be accepted.
  • 4: Strong: I learned a lot from it. I would like to see it accepted.
  • 3.5: Leaning positive: It can be accepted more or less in its current form. However, the work it describes is not particularly exciting and/or inspiring, so it will not be a big loss if people don’t see it in this conference.
  • 3: Ambivalent: It has merits (e.g., it reports state-of-the-art results, the idea is nice), but there are key weaknesses (e.g., I didn’t learn much from it, evaluation is not convincing, it describes incremental work). I believe it can significantly benefit from another round of revision, but I won’t object to accepting it if my co-reviewers are willing to champion it.
  • 2.5: Leaning negative: I am leaning towards rejection, but I can be persuaded if my co-reviewers think otherwise.
  • 2: Mediocre: I would rather not see it in the conference.
  • 1.5: Weak: I am pretty confident that it should be rejected.
  • 1: Poor: I would fight to have it rejected.