MAXENT seems to outperform the CLASS BASED baseline because it learns more from the training data.

' —E|— MaxEnt —@— ClassBased I I I I I I I I I I I I I I I I I I I I I I I I I

' —E|— MaXEnt —9— ClassB ased

To evaluate our system ( MAXENT ) and our baselines, we partitioned the corpora into training and testing data.

For each NP in the test data, we generated a set of modifiers and looked at the predicted orderings of the MAXENT , CLASS BASED, and GOOGLE N-GRAM methods.

The MAXENT model consistently outperforms CLASS BASED across all test corpora and sequence lengths for both tokens and types, except when testing on the Brown and Switchboard corpora for modifier sequences of length 5, for which neither approach is able to make any correct predictions.

MAXENT also outperforms the GOOGLE N-GRAM baseline for almost all test corpora and sequence lengths.

For the Switchboard test corpus token and type accuracies, the GOOGLE N-GRAM baseline is more accurate than MAXENT for sequences of length 2 and overall, but the accuracy of MAXENT is competitive with that of GOOGLE N-GRAM.

Maximum entropy ( MaxEnt ) models1 have been widely used in many NLP tasks (Berger et al., 1996; Ratnaparkhi, 1997; Smith, 2006).

With MaxEnt , we learn from the input data:

When 11 is 0, the algorithm ignores the unlabeled data and degenerates to two MaXEnt models trained on only the labeled data.

MaxEnt: This method learns a MaxEnt classifier for each language given the monolingual labeled data; the unlabeled data is not used.

8 By making use of the unlabeled parallel data, our proposed approach improves the accuracy, compared to MaXEnt , by 8.12% (or 33.27% error reduction) on English and 3.44% (or 16.92% error reduction) on Chinese in the first setting, and by 5.07% (or 19.67% error reduction) on English and 3.87% (or 19.4% error reduction) on Chinese in the second setting.

8Significance is tested using paired t-tests with p<0.05: denotes statistical significance compared to the corresponding performance of MaXEnt ; * denotes statistical significance compared to SVM; and r denotes statistical significance compared to Co-SVM.

When 11 is set to 0, the joint model degenerates to two MaXEnt models trained with only the labeled data.

We achieved a high accuracy of 84.7% for predicting such boundaries using MaXEnt model on machine parse trees.

During training, we label nodes with translation boundaries, as one additional fitnction tag; during decoding, we employ the MaxEnt model to predict the translation boundary label probability for each span associated with a subgraph y, and discourage derivations accordingly for using nonterminals over the non—translation boundary span.

To learn our MaxEnt models defined in § 3.3, we collect the events during extracting elm2str grammar in training time, and learn the model using improved iterative scaling.

There are 16 thousand human parse trees with human alignment; additional 1 thousand human parse and aligned sent-pairs are used as unseen test set to verify our MaxEnt models and parsers.

It showed our MaxEnt model is very accurate using human trees: 94.5% of accuracy, and about 84.7% of accuracy for using the machine parsed trees.

The boundary cases were not addressed in the previous literature for trees, and here we include them in our feature sets for learning a MaxEnt model to predict the transformations.

The rest of the paper is organized as follows: in section 2, we analyze the projectable structures using human aligned and parsed data, to identify the problems for SCFG in general; in section 3, our proposed approach is explained in detail, including the statistical operators using a MaxEnt model; in section 4, we illustrate the integration of the proposed approach in our decoder; in section 5, we present experimental results; in section 6, we conclude with discussions and future work.