Difference between revisions of "Comparative Study of Discriminative Models in SMT"
| Line 1: | Line 1: | ||
=== Summary === | === Summary === | ||
| − | This page compares and contrasts two discriminative methods for [[ | + | This page compares and contrasts two discriminative methods for [[AddressesProblem::Machine Translation]] that have been proposed in [[An_End-to-End_Discriminative_Approach_to_Machine_Translation]] and [[A_Discriminative_Latent_Variable_Model_for_SMT]]. The main different between these methods is in the approach taken for building the translation model. In the former case, a vector of features is trained using parallel data in order to maximize the likelihood of the data, and weight vector is trained using a weighted perceptron method on a separate phase. On the other hand, the latter work employs a log-linear model, where the feature set and the weights are trained jointly in order to maximize the likelihood of the data. |
We will call the work in [[An_End-to-End_Discriminative_Approach_to_Machine_Translation]], "A" and the work in [[A_Discriminative_Latent_Variable_Model_for_SMT]] "B". | We will call the work in [[An_End-to-End_Discriminative_Approach_to_Machine_Translation]], "A" and the work in [[A_Discriminative_Latent_Variable_Model_for_SMT]] "B". | ||
Revision as of 23:23, 5 November 2012
Summary
This page compares and contrasts two discriminative methods for Machine Translation that have been proposed in An_End-to-End_Discriminative_Approach_to_Machine_Translation and A_Discriminative_Latent_Variable_Model_for_SMT. The main different between these methods is in the approach taken for building the translation model. In the former case, a vector of features is trained using parallel data in order to maximize the likelihood of the data, and weight vector is trained using a weighted perceptron method on a separate phase. On the other hand, the latter work employs a log-linear model, where the feature set and the weights are trained jointly in order to maximize the likelihood of the data.
We will call the work in An_End-to-End_Discriminative_Approach_to_Machine_Translation, "A" and the work in A_Discriminative_Latent_Variable_Model_for_SMT "B".
Minor Differences
- The baseline models used in these 2 papers are different statistical machine translations models. The work in A uses phrase based models proposed in Koehn_et_al,_ACL_2003
