For the 44 and 230 million tokens corpora, all sentences are automatically parsed and used to initialize model parameters , while for 1.3 billion tokens corpus, we parse the sentences from a portion of the corpus that

contain 230 million tokens, then use them to initialize model parameters .

Nevertheless, experimental results show that this approach is effective to provide initial values of model parameters .

The objective of maximum likelihood estimation is to maximize the likelihood £(D, p) respect to model parameters .

and denote ’2' N as the collection of N -best list parse trees for sentences over entire corpus D under model parameter p.

mate model parameters .

From these corpora, we estimate translation model parameters : word-to-word translation tables, fertilities, distortion parameters, phrase tables, syntactic transformations, etc.

A language model P (e) is typically used in SMT decoding (Koehn, 2009), but here P (6) actually plays a central role in training translation model parameters .

During decipherment training, our objective is to estimate the model parameters 0 in order to maximize the probability of the foreign corpus f. From Equation 4 we have:

5 For Bayesian MT decipherment, we set a high prior value on the language model (104) and use sparse priors for the IBM 3 model parameters t, n, d,p (0.01, 0.01, 0.01, 0.01).

During decipherment, our goal is to estimate the channel model parameters 6.

These methods are attractive for their ability to manage uncertainty about model parameters and allow one to incorporate prior knowledge during inference.

Since our model is a nonlinear approximation of the underlying action-value function of the game, we learn model parameters by applying nonlinear regression to the observed final utilities from the simulated roll-outs.

The resulting update to model parameters 6 is of the form:

We use the same experimental settings across all methods, and all model parameters are initialized to zero.

Basic Interpolation: This smoothing model, Pinw(e|q), linearly combines our foreground and background models using a model parameter 04:

Section 5.2 outlines our procedure for leam-ing the model parameters for both 15mm(e|q) and

5.2.1 Model Parameters

These methods are attractive for their ability to manage uncertainty about model parameters and allow one to incorporate prior knowledge during inference.

Our goal is to estimate the channel model parameters 6 in order to maximize the probability of the observed ciphertext c:

The base distribution P0 represents prior knowledge about the model parameter distributions.