Vocabulary Manipulation for Neural Machine Translation

Understand

In order to capture rich language phenomena, neural machine translation models have to use a large vocabulary size, which requires high computing time and large memory usage.

• In this paper, we alleviate this issue by introducing a sentence-level or batch-level vocabulary, which is only a very small sub-set of the full output vocabulary.
• For each sentence or batch, we only predict the target words in its sentence-level or batch-level vocabulary.
• Thus, we reduce both the computing time and the memory usage.