Visualization-based improvement of neural machine translation. (April 2022)

• Record Type: Journal Article
• Authors: Munz, Tanja; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel
• Is Part Of: Computers & graphics. Volume 103(2022)
• Page Start: 45
• Page End: 60
• Publication Date: 2022-04
• Subjects: Neural machine translation -- Machine learning -- Visual analytics -- LSTM -- Transformer -- Translation correction; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2021.12.003
• Languages: English
• ISSNs: 0097-8493

Abstract

Abstract: We introduce a novel visual-interactive 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- or matrix-based visualizations 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. Furthermore, we performed quantitative computer-based experiments that show that our system can be used to improve translation quality and reduce post-editing efforts for domain-specific documents. Graphical abstract: Highlights: A visual-interactive approach for analyzing, understanding, and correcting NMT. Visualizations show model-specific information and metrics for translation quality. Our approach supports both LSTM-based NMT models and the Transformer architecture. The quantitative computer-based evaluation reveals reduced post-editing efforts.