Learning from Chunk-based Feedback in Neural Machine Translation
This addresses domain adaptation for machine translation users, but it is incremental as it builds on existing feedback methods.
The paper tackles the problem of domain mismatch in neural machine translation by using chunk-level user feedback, achieving a 2.61% absolute BLEU improvement over sentence-based feedback in simulation experiments.
We empirically investigate learning from partial feedback in neural machine translation (NMT), when partial feedback is collected by asking users to highlight a correct chunk of a translation. We propose a simple and effective way of utilizing such feedback in NMT training. We demonstrate how the common machine translation problem of domain mismatch between training and deployment can be reduced solely based on chunk-level user feedback. We conduct a series of simulation experiments to test the effectiveness of the proposed method. Our results show that chunk-level feedback outperforms sentence based feedback by up to 2.61% BLEU absolute.