Abstract
We present a method for finding oracle BLEU translations in phrase-based statistical machine translation using exact document-level scores. Experiments are presented where the BLEU score of a candidate translation is directly optimised in order to examine the properties of reachable translations with very high BLEU scores. This is achieved by running the document-level decoder Docent in BLEU-decoding mode, where proposed changes to the translation of a document are only accepted if they increase BLEU. The results confirm that the reference translation cannot in most cases be reached by the decoder, which is limited by the set of phrases in the phrase table, and demonstrate that high-BLEU translations are often of poor quality.
| Original language | English |
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| Title of host publication | Proceedings of the 19th Annual Conference of the European Association for Machine Translation, EAMT 2016 |
| Publication date | 1 Jun 2016 |
| Publication status | Published - 1 Jun 2016 |
| Externally published | Yes |