A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation (2020.aacl-main)
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| Challenge: | Despite the success of neural machine translation, simultaneous neural machine translators are challenging due to syntactic structure difference and simultaneity requirements. |
| Approach: | They propose a framework for adapting neural machine translation to translate simultaneously . they propose 'prefix translation' that utilizes a consecutive NMT model to translate source prefixes . |
| Outcome: | The proposed framework balancing quality and latency on three translation corpora and two language pairs shows that it performs well. |
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