  Through the development of neural machine translation, the quality of machine
translation systems has been improved significantly. By exploiting advancements
in deep learning, systems are now able to better approximate the complex
mapping from source sentences to target sentences. But with this ability, new
challenges also arise. An example is the translation of partial sentences in
low-latency speech translation. Since the model has only seen complete
sentences in training, it will always try to generate a complete sentence,
though the input may only be a partial sentence. We show that NMT systems can
be adapted to scenarios where no task-specific training data is available.
Furthermore, this is possible without losing performance on the original
training data. We achieve this by creating artificial data and by using
multi-task learning. After adaptation, we are able to reduce the number of
corrections displayed during incremental output construction by 45%, without a
decrease in translation quality.
