When and Why is Document-level Context Usefulin Neural Machine Translation?
Tabelle 4: Comparison of context word filtering methods.
['Context sentence', '[BOLD] en-it Bleu [%]', '[BOLD] en-it Ter [%]', '[BOLD] en-de Bleu [%]', '[BOLD] en-de Ter [%]', '#tokens']
All filtering methods shrink the context input drastically without a significant loss of performance. Each method has its own motivation to retain only useful tokens in the context; the results show that they are all reasonable in practice. In particular, using only named entities as context input, we achieve the same level of improvement with only 13% of tokens in the full context sentences. By filtering words in the context sentences, we can use more examples in each batch for a robust training.