Exact Decoding with Multi Bottom-Up Tree Transducers

Abstract

We present an experimental statistical tree-to-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. * This work was supported by Deutsche Forschungsgemeinschaft grant MA/4959/1-1. 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. 2 A translation is sensible if it is of linear size increase and can be computed by some (potentially copying) top-down tree transducer.

Improved N-Best Extraction with an Evaluation on Language Data

“…This data set consists of tree automata resulting from an English-to-German machine-translation task, described in the doctoral thesis of Quernheim (2017).…”