Debugging Neural Machine Translations

8 Aug 2018  ·  Matīss Rikters ·

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: http://attention.lielakeda.lv read more

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Machine Translation WMT 2017 Latvian-English RNN BLEU - # 4

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