Papers with high-quality translation systems
Lost in Machine Translation: A Method to Reduce Meaning Loss (N19-1)
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| Challenge: | state-of-the-art translation systems often fail in preserving meaning . ambiguity between source and target languages can cause translation problems . |
| Approach: | They propose to use a pre-trained neural sequence-to-sequence model to define a less ambiguous translation system. |
| Outcome: | The proposed system preserves meaning in two languages without compromising translation quality. |