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Moses: Open Source Toolkit for Statistical Machine Translation
We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c)Expand
Statistical Phrase-Based Translation
The empirical results suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translation. Expand
Europarl: A Parallel Corpus for Statistical Machine Translation
A corpus of parallel text in 11 languages from the proceedings of the European Parliament is collected and its acquisition and application as training data for statistical machine translation (SMT) is focused on. Expand
Abstract Meaning Representation for Sembanking
A sembank of simple, whole-sentence semantic structures will spur new work in statistical natural language understanding and generation, like the Penn Treebank encouraged work on statistical parsing. Expand
Pharaoh: A Beam Search Decoder for Phrase-Based Statistical Machine Translation Models
We describe Pharaoh, a freely available decoder for phrase-based statistical machine translation models. The decoder is the implement at ion of an efficient dynamic programming search algorithm withExpand
Statistical Significance Tests for Machine Translation Evaluation
If two translation systems differ differ in performance on a test set, can we trust that this indicates a difference in true system quality? To answer this question, we describe bootstrap resamplingExpand
Clause Restructuring for Statistical Machine Translation
The reordering approach is applied as a pre-processing step in both the training and decoding phases of a phrase-based statistical MT system, showing an improvement from 25.2% Bleu score for a baseline system to 26.8% Blee score for the system with reordering. Expand
Factored Translation Models
In a number of experiments, it is shown that factored translation models lead to better translation performance, both in terms of automatic scores, as well as more grammatical coherence. Expand
Findings of the 2012 Workshop on Statistical Machine Translation
A large-scale manual evaluation of 103 machine translation systems submitted by 34 teams was conducted, which used the ranking of these systems to measure how strongly automatic metrics correlate with human judgments of translation quality for 12 evaluation metrics. Expand