  We present a survey covering the state of the art in low-resource machine
translation research. There are currently around 7000 languages spoken in the
world and almost all language pairs lack significant resources for training
machine translation models. There has been increasing interest in research
addressing the challenge of producing useful translation models when very
little translated training data is available. We present a summary of this
topical research field and provide a description of the techniques evaluated by
researchers in several recent shared tasks in low-resource MT.
