Robert Frost once said, “Poetry is what gets lost in translation”. Translating poetry is a very hard task even for humans, and is clearly beyond the capability of current machine translation systems. We therefore, out of academic curiosity, set about testing the limits of translating poetry and were pleasantly surprised with the results!
A Statistical Machine Translation system, like Google Translate, typically performs translations by searching through a multitude of possible translations, guided by a statistical model of accuracy. However, to translate poetry, we not only considered translation accuracy, but meter and rhyming schemes as well. In our paper we describe in more detail how we altered our translation model, but in general we chose to sacrifice a little of the translation’s accuracy to get the poetic form right.
As a pleasant side-effect, the system is also able to translate anything into poetry, allowing us to specify the genre (say, limericks or haikus), or letting the system pick the one it thinks fits best. At the moment, the system is too slow to be made publicly accessible, but we thought we’d share some excerpts…
The somewhat more technical paper is available as a pdf, and one person even wrote a clever commentary in verse.