Visual-Interactive Neural Machine Translation

We introduce a novel visual analytics approach for analyzing, understanding, and correcting neural machine translation. Our system supports users in automatically translating documents using neural machine translation and identifying and correcting possible erroneous translations. User corrections can then be used to fine-tune the neural machine translation model and automatically improve the whole document. While translation results of neural machine translation can be impressive, there are still many challenges such as over- and under-translation, domain-specific terminology, and handling long sentences, making it necessary for users to verify translation results; our system aims at supporting users in this task. Our visual analytics approach combines several visualization techniques in an interactive system. A parallel coordinates plot with multiple metrics related to translation quality can be used to find, filter, and select translations that might contain errors. An interactive beam search visualization and graph visualization for attention weights can be used for post-editing and understanding machine-generated translations. The machine translation model is updated from user corrections to improve the translation quality of the whole document. We designed our approach for an LSTM-based translation model and extended it to also include the Transformer architecture. We show for representative examples possible mistranslations and how to use our system to deal with them. A user study revealed that many participants favor such a system over manual text-based translation, especially for translating large documents.

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@inproceedings{nmtvis2021,
  author = {Munz, Tanja and Väth, Dirk and Kuznecov, Paul and Vu, Ngoc Thang and Weiskopf, Daniel},
  title = {Visual-Interactive Neural Machine Translation},
  booktitle = {Proceedings of Graphics Interface 2021},
  year = {2021},
  pages = {265 -- 274},
  publisher = {Canadian Information Processing Society},
  location  = {Virtual Event},
  doi = {10.20380/GI2021.30},
}

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