Debugging Neural Machine Translations

By Matīss Rikters. 2018

5 citations recorded by the source corpus.

In this paper, we describe a tool for debugging the output and attention weights of neural machine translation (NMT) systems and for improved estimations of confidence about the output based on the attention. The purpose of the tool is to help researchers and developers find weak and faulty example translations that their NMT systems produce without the need for reference translations. Our tool also includes an option to directly compare translation outputs from two different NMT engines or experiments. In addition, we present a demo website of our tool with examples of good and bad translations: this http URL

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

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Citations in the corpus

Analysis Methods in Neural Language Processing: A Survey (2018, methodology): “Another tool focused on comparing attention alignments was proposed by Rikters (2018).”
Neural Machine Translation: A Review (2019, background): “The only work on confidence in NMT we are aware of is from Rikters and Fishel [372], Rikters [373] who aim to use attention to estimate word-level confidences.”