Reinforcement Learning for on-line Sequence Transformation

In simultaneous machine translation (SMT), an output sequence should be produced as soon as possible, without reading the whole input sequence. This requirement creates a trade-off between translation delay and quality because less context may be known during translation. In most SMT methods, this trade-off is controlled with parameters whose values need to be tuned. In this paper, we introduce an SMT system that learns with reinforcement and is able to find the optimal delay in training. We conduct experiments on Tatoeba and IWSLT2014 datasets against state-of-the-art translation architectures. Our method achieves comparable results on the former dataset, with better results on long sentences and worse but comparable results on the latter dataset.