Statistical Phrase-Based Translation 
 We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models . Within our framework, we carry out a large number of experiments to understand better and explain why phrase-based models outperform word-based models . Our empirical results, which hold for all examined language pairs , 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 translations . Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy word-level alignment models does not have a strong impact on performance. Learning only syntactically motivated phrases degrades the performance of our systems. 