Although the log-linear model achieves success in SMT, it still suffers from some limitations: (1) the features are required to be linear with respect to the model itself; (2) features cannot be further interpreted to reach their potential.

Recently, great progress has been achieved in SMT, especially since Och and Ney (2002) proposed the log-linear model: almost all the state-of-the-art SMT systems are based on the log-linear model .

Regardless of how successful the log-linear model is in SMT, it still has some shortcomings.

Compared with the log-linear model , it has more powerful expressive abilities and can deeply interpret and represent features with hidden units in neural networks.

Instead, we assume that the distribution over yCFG is a log-linear model with parameters 601:0 (i.e., a sub-vector of 6) , namely:

In this setup, the model reduces to a normal log-linear model for the target formalism.

It’s not surprising that Cahill’s model outperforms our log-linear model because it relies heavily on handcrafted rules optimized for the dataset.

Feature functions in log-linear models are designed to capture the characteristics of each derivation in the tree.

log-linear models with parameters, AiméRM , is

labeled data, 712?, based on the log-linear model in Eq.

The a: is used as the input matrix of the kth log-linear model (corresponding to kth semantic tag (topic)) to infer the [3 hyper-parameter of MTR in Eq.

We then report the corresponding chains 0(a) as the system output.3 For learning, the gradient takes the standard form of the gradient of a log-linear model , a difference of expected feature counts under the gold annotation and under no annotation.

We use a log-linear model that can be expressed as a factor graph.

The final log-linear model is given by the following formula:

Given a single instruction 2' with category au, we use a log-linear model to represent the distri-

We employ a log-linear model and try to maximize initial dialog state distribution over the space of all nodes in a dialog network:

Dialog State Update Model We use a log-linear model to maximize a dialog state distribution over the space of all nodes in a dialog network:

But instead of using just the PMI scores of bilingual NE pairs, as in our work, they employed a feature-rich log-linear model to capture bilingual correlations.

Parameters in their log-linear model require training with bilingually annotated data, which is not readily available.

(2010a) presented a supervised learning method for performing joint parsing and word alignment using log-linear models over parse trees and an ITG model over alignment.