Medium-scale experiments show an absolute and statistically significant improvement of +0.7 BLEU points over a state-of-the-art forest-based tree-to-string system even with fewer rules.

With the help of the dependency language model, our new model achieves a significant improvement of +0.7 BLEU points over the forest 625 baseline system (p < 0.05, using the sign-test suggested by

Both string-to-constituency system (e.g., (Galley et al., 2006; Marcu et al., 2006)) and string-to-dependency model (Shen et al., 2008) have achieved significant improvements over the state-of-the-art formally syntax-based system Hiero (Chiang, 2007).

Medium data experiments (Section 5) show a statistically significant improvement of +0.7 BLEU points over a state-of-the-art forest-based tree-to-string system even with less translation rules, this is also the first time that a tree-to-tree model can surpass tree-to-string counterparts.

(2009), their forest-based constituency-to-constituency system achieves a comparable performance against Moses (Koehn et al., 2007), but a significant improvement of +3.6 BLEU points over the 1-best tree-based constituency-to-constituency system.

This model shows a significant improvement over the state-of-the-art hierarchical phrase-based system (Chiang, 2005).

The experiment shows tree kernel approach is able to give statistical significant improvements over flat syntactic path feature.

Besides, we further propose to leverage on temporal ordering information to constrain the interpretation of discourse relation, which also demonstrate statistical significant improvements for discourse relation recognition on PDTB 2.0 for both explicit and implicit as well.

The experimental results on PDTB v2.0 show that our kernel-based approach is able to give statistical significant improvement over flat syntactic path method.

In addition, we also propose to incorporate temporal ordering information to constrain the interpretation of discourse relations, which also demonstrate statistical significant improvements for discourse relation recognition, both explicit and implicit.

The experiment shows that tree kernel is able to effectively incorporate syntactic structural information and produce statistical significant improvements over flat syntactic path feature for the recognition of both explicit and implicit relation in Penn Discourse Treebank (PDTB; Prasad et al., 2008).

Besides, inspired by the linguistic study on tense and discourse anaphor (Webber, 1988), we further propose to incorporate temporal ordering information to constrain the interpretation of discourse relation, which also demonstrates statistical significant improvements for discourse relation recognition on PDTB v2.0 for both explicit and implicit relations.

likelihood ratio is significant, then this indicates that the new factor significantly improves model fit.

The addition of the semantic factor significantly improves model fit for both the simple semantic space and LDA.

Considering the trigram model first, we find that adding this factor to the model gives a significant improvement in fit.

As far as LDA is concerned, the additive model significantly improves model fit, whereas the multiplicative one does not.

Extensive experiments on large-scale bidirectional Japanese-English translations testified the significant improvements on BLEU score.

Comparing the BLEU-4 scores of PTT+C'3;g and PTT+03, we gained 0.56 (t2s) and 0.57 (s2t) BLEU-4 points which are significant improvements (p < 0.05).

Furthermore, we gained 0.50 (t2s) and 0.62 (s2t) BLEU-4 points from PTT+FS to PTT+F, which are also significant improvements (p < 0.05).

By introducing supertags into the target language side, i.e., the target language model and the target side of the phrase table, significant improvement was achieved for Arabic-to-English translation.

(2007) also reported a significant improvement for Dutch-English translation by applying CCG supertags at a word level to a factorized SMT system (Koehn et al., 2007).

Significant improvements are obtained over a state-of-the—art hierarchical phrase-based machine translation system.

We saw that the bilingual sense similarity computed by our algorithm led to significant improvements .

Alg2 significantly improved the performance over the baseline.

We can see that IBM model 1 and cosine distance similarity function both obtained significant improvement on all test sets of the two tasks.

In addition, when integrated into a 2nd-ordered MST parser, the projected parser brings significant improvement to the baseline, especially for the baseline trained on smaller treebanks.

It indicates that, the smaller the human-annotated treebank we have, the more significant improvement we can achieve by integrating the projecting classifier.

More importantly, when this classifier is integrated into a 2nd-ordered maXimum spanning tree (MST) dependency parser (McDonald and Pereira, 2006) in a weighted average manner, significant improvement is obtained over the MST baselines.

Unfortunately, we found in our experiments that the pairs extracted using the method are too noisy for reliable error model training, even with a very tight threshold, and we did not see any significant improvement .

a new phrase-based error model, which leads to significant improvement in our spelling correction experiments.

Results show that the error models learned from clickthrough data lead to significant improvements on the task of query spelling correction.

The experimental results show that our approach achieves a significant improvement on both gold standard tree bank and automatically parsed tree pairs against a heuristic similarity based method.

By introducing BTKs to construct a composite kernel, the performance in both corpora is significantly improved against only using the polynomial kernel for plain features.

Recent research on tree based systems shows that relaxing the restriction from tree structure to tree sequence structure (Synchronous Tree Sequence Substitution Grammar: STSSG) significantly improves the translation performance (Zhang et al., 2008).

Using logistic regression instead of the untrained weights significantly improves performance.

Using active learning also significantly improves performance: L(MWD,+) outscores L(MWD,—) by 13%.

We expect that adding these types of data would significantly improve our system.

The experimental results on three NIST evaluation test sets show that our method leads to significant improvements in translation accuracy over the baseline systems.

It also gives us a rational eXplanation for the significant improvements achieved by our method as shown in Section 5.3.

Experimental results show that our method leads to significant improvements in translation accuracy over the baseline systems.