This paper presents a framework for statistical machine translation (SMT) based on direct maximum entropy models, which includes the widely used source-channel approach as a special case. The framework treats all knowledge sources as feature functions that depend on the source and target sentences, as well as possible hidden variables. This allows for easy extension of a baseline SMT system by adding new feature functions. The authors show that this approach significantly improves baseline SMT systems. The paper discusses the source-channel approach, which involves maximizing the product of the language model and the translation model. However, this approach has several limitations, including the need for true probability distributions and the difficulty of extending the model. An alternative approach is the direct maximum entropy translation model, which directly models the posterior probability $ Pr(e_{1}^{I}|f_{1}^{J}) $. This model uses maximum entropy to estimate the probability distribution, allowing for more flexibility and better performance. The paper also introduces alignment templates, which are pairs of source and target language phrases with alignments between words. These templates are used to refine the translation probability. The authors propose using multiple feature functions in the direct maximum entropy model, including sentence length, language models, and lexical features. These features help improve the translation quality by considering word context and grammatical dependencies. The training process uses the GIS (Generalized Iterative Scaling) algorithm to optimize the model parameters. The authors also address the issue of reference translations and propose using multiple references to improve the evaluation of translation results. The results show that the direct maximum entropy approach outperforms the source-channel approach on various error metrics, including SER, WER, PER, mWER, BLEU, and IER. The paper concludes that the direct maximum entropy approach is more general and flexible than the source-channel approach, allowing for easier extension with new features. It also highlights the importance of handling complex features in search and optimizing parameters directly with respect to the error rate. The authors suggest that further research is needed to address these challenges.This paper presents a framework for statistical machine translation (SMT) based on direct maximum entropy models, which includes the widely used source-channel approach as a special case. The framework treats all knowledge sources as feature functions that depend on the source and target sentences, as well as possible hidden variables. This allows for easy extension of a baseline SMT system by adding new feature functions. The authors show that this approach significantly improves baseline SMT systems. The paper discusses the source-channel approach, which involves maximizing the product of the language model and the translation model. However, this approach has several limitations, including the need for true probability distributions and the difficulty of extending the model. An alternative approach is the direct maximum entropy translation model, which directly models the posterior probability $ Pr(e_{1}^{I}|f_{1}^{J}) $. This model uses maximum entropy to estimate the probability distribution, allowing for more flexibility and better performance. The paper also introduces alignment templates, which are pairs of source and target language phrases with alignments between words. These templates are used to refine the translation probability. The authors propose using multiple feature functions in the direct maximum entropy model, including sentence length, language models, and lexical features. These features help improve the translation quality by considering word context and grammatical dependencies. The training process uses the GIS (Generalized Iterative Scaling) algorithm to optimize the model parameters. The authors also address the issue of reference translations and propose using multiple references to improve the evaluation of translation results. The results show that the direct maximum entropy approach outperforms the source-channel approach on various error metrics, including SER, WER, PER, mWER, BLEU, and IER. The paper concludes that the direct maximum entropy approach is more general and flexible than the source-channel approach, allowing for easier extension with new features. It also highlights the importance of handling complex features in search and optimizing parameters directly with respect to the error rate. The authors suggest that further research is needed to address these challenges.