It is a well-known fact that when you have a hammer, everything looks like a nail. It is a less-known fact that when you have a sequence-to-sequence model, everything looks like machine translation. One example of this thinking is the paper Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity recently uploaded to arXiv by researchers from Johns Hopkins University.
The paper approaches the task of paraphrase generation, i.e., for a source sentence, they want to generate a target sentence in the same language, with the meaning as similar as possible to the source sentence, but worded as differently as possible. Their approach does not need any training examples of paraphrased sentence pairs. It only needs a multilingual machine translation system (which is indeed a complex system that not everyone just has on their hard drives for case). The training requires plenty of parallel sentences (i.e., sentences which are a translation of each other) in several languages. It is hard to say what sort of data is easier to obtain: whether mutual paraphrases or mutual translations, but I would probably vote in favor of the translation.
The paper creatively reiterates the idea of zero-shot machine translation. In such a setup, we only have parallel data for some language pairs and we train a single model to translate between all of them. To do so, the model needs to be told what is the source language and what is the target language and it uses special symbols appended to the input for that. When this is trained properly, we can tell the model to translate between two languages it was never presented together at the training time, but only within different language pairs. Something like what is shown in the following scheme (from MT Weekly 7 about zero-shot translation):
This is basically the model they train in this paper. However, in the end, they tell the model to translate from English into English, and this is how they get the paraphrases.
This is cool, but there is nothing telling the model that output should be worded differently than the input. It appears to have a simple solution which is the second innovation of the paper. They introduce a simple modification of the beam search algorithm such that it penalizes using word n-grams that are in the source sentence. We can thus view the beam search as optimization of two opposing objectives: the probability given the model, and dissimilarity from the source. And this is it! This how the best current paraphrasing system works (although it is hard to say what the best means because the evaluation of paraphrases is quite tricky).
I like the paper because it shows a creative way of using existing models. The models are trained to solve some specific tasks, but to so, they must be aware of plenty of other things. Being able to hack the models and get what is hidden inside is just cool. It shows that neural models are no longer total black-boxes, so we can bend them such that they do what we want.
Dual-use disclaimer: Dear plagiarists, academic pirates, and lazy students, this is a tool for you! But don’t overdo it, machine-generated text can be easily automatically recognized.