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It's a well-known fact that if you have a hammer, everything looks like a nail. It is a lesser-known fact that everything looks like machine translation for a sequence-to-sequence model. An example of this thinking is the paper paraphras generation as zero-shot multilingual translation: Disference Semantic similarity of lexical and syntactic variety, recently uploaded in Axiv from researchers from Johns Hopkins University.
The paper approaches the task of rewriting generation, d. H. For a source set, you want to generate a target set in the same language, the meaning being generated as much as possible to the source set, but as varied as possible. Your approach requires no educational examples of paraphrased sentence pairs. It only needs a multilingual machine translation system (which is indeed a complex system that does not everyone only have on their hard drives in case). The training requires many parallel phrases (d. H. sentences that are a translation from each other) in several languages. It's hard to say what kind of data is easier to get: whether mutual paraphrases or mutual translations, but I would probably vote in favor of translation.
The paper creatively reaffirms the idea of ??zero-shot machine translation. In such a setup, we only have parallel data for some language pairs and train a single model to translate between all. For this, the model must be said what the source language is and what is the target language and the target language is and used special symbols that are attached to the receipt. If this is trained correctly, we can tell the model to translate between two languages, which it was never put together during the training time, but only in different language pairs. Something like in the following control (from MT weekly 7 over zero-shot translation):
This is basically the model you train in this paper. In the end, however, they say the model to translate into English from English, and so they get the paraphrases.
That's cool, but there is nothing, what the model does not say that the output should be formulated differently than the input. It seems to have a simple solution that is the second innovation of the paper. They perform a simple change in the blast search algorithm so that it is punished with Word n -grams, which are in the source set. We can therefore display the beam search as an optimization of two opposing goals: the probability that offers the model and the dissimilarity from the source. And that's it! How the best current paraphrase system works (though it's hard to say what the best means, because the evaluation of paraphrases is quite difficult).
I like the paper because it shows a creative way of using existing models. The models are trained to solve some specific tasks, but so they must be aware of many other things. To be able to chop the models and get what is hidden in is just cool. It shows that neuronal models are no longer total black boxes, so we can bend them so that they do what we want.
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