Learning from Chunk-based Feedback in Neural Machine Translation

By Pavel Petrushkov, Shahram Khadivi, Evgeny Matusov. 2018

19 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Machine Translation