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• W2250548645 abstract "In this paper we describe Travatar, a forest-to-string machine translation (MT) engine based on tree transducers. It provides an open-source C++ implementation for the entire forest-to-string MT pipeline, including rule extraction, tuning, decoding, and evaluation. There are a number of options for model training, and tuning includes advanced options such as hypergraph MERT, and training of sparse features through online learning. The training pipeline is modeled after that of the popular Moses decoder, so users familiar with Moses should be able to get started quickly. We perform a validation experiment of the decoder on EnglishJapanese machine translation, andfind that it is possible to achieve greater accuracy than translation using phrase-based and hierarchical-phrase-based translation. As auxiliary results, we also compare different syntactic parsers and alignment techniques that we tested in the process of developing the decoder. Travatar is available under the LGPL at http://phontron.com/travatar" @default.
• W2250548645 created "2016-06-24" @default.
• W2250548645 creator A5068811427 @default.
• W2250548645 date "2013-08-01" @default.
• W2250548645 modified "2023-09-26" @default.
• W2250548645 title "Travatar: A Forest-to-String Machine Translation Engine based on Tree Transducers" @default.
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• W2250548645 hasPublicationYear "2013" @default.
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