Large-scale Exact Decoding: The IMS-TTT submission to WMT14
By Daniel Quernheim, Fabienne Cap. 2014
4 citations recorded by the source corpus.
We present the IMS-TTT submission to WMT14, an experimental statistical treeto-tree machine translation system based on the multi-bottom up tree transducer including rule extraction, tuning and decoding. Thanks to input parse forests and a “no pruning” strategy during decoding, the obtained translations are competitive. The drawbacks are a restricted coverage of 70% on test data, in part due to exact input parse tree matching, and a relatively high runtime. Advantages include easy redecoding with a different weight vector, since the full translation forests can be stored after the first decoding pass.
Subfields: Machine Translation
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In Machine Translation
Citations in the corpus
- Exact Decoding with Multi Bottom-Up Tree Transducers (2015, methodology): “(1)The system presented in this paper is variant of the system presented at last year’s workshop (Quernheim and Cap, 2014), without morphological enhancements.”