This is a conditional log-linear model parameterized by qb, where gbk, ~ N(0, 0,3).

For learning, we iteratively adjust our model’s parameters to better explain our samples.

(2012) we use topics as the contexts, but learn mention topics jointly with other model parameters .

E-step: Collect samples by MCMC simulation as in §5, given current model parameters 6 and qb.

Unsupervised Grammar Induction Our first method for grammar induction is fully unsupervised Viterbi EM training of the Dependency Model with Valence (DMV) (Klein and Manning, 2004), with uniform initialization of the model parameters .

(2010a) show that Viterbi (hard) EM training of the DMV with simple uniform initialization of the model parameters yields higher accuracy models than standard soft-EM

In Viterbi EM, the E—step finds the maximum likelihood corpus parse given the current model parameters .

We should note that since our model parameter A is represented and learned in the low-rank form, we only have to store and maintain the low-rank projections U gbh, ngm and nghm rather than explicitly calculate the feature tensor gbh®gbm®gbh,m.

We will directly learn a low-rank tensor A (because r is small) in this form as one of our model parameters .

where 6 6 RL, U 6 RM”, V 6 RM”, and W E 1Rer are the model parameters to be learned.

gym] is the plausible score for the best translation candidate given the model parameters W and V .

Table 1: The relationship between the size of training data and the number of model parameters .

Table 1 shows the relationship between the size of training data and the number of model parameters .

This can explain the success of response-based learning: Lexical and structural variants of reference translations can be used to boost model parameters towards translations with positive feedback, while the same translations might be considered as negative examples in standard structured learning.

Here, leam-ing proceeds by “trying out” translation hypotheses, receiving a response from interacting in the task, and converting this response into a supervision signal for updating model parameters .

(2010) or Goldwasser and Roth (2013) describe a response-driven learning framework for the area of semantic parsing: Here a meaning representation is “tried out” by itera-tively generating system outputs, receiving feedback from world interaction, and updating the model parameters .

For example, in Expectation-Maximization (Dempster et al., 1977), the Expectation (E) step computes the posterior distribution over possible completions of the data, and the Maximization (M) step reestimates the model parameters as

Different models parameterize this probability distribution in different ways.

It also contains most of the model’s parameters and is where overfitting occurs most.