Abstract | 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. |
Experiments | As shown in the third line in the column of BLEU score, the performance drops 1.7 BLEU points over baseline system due to the poorer rule coverage. |
Experiments | This suggests that using dependency language model really improves the translation quality by less than 1 BLEU point . |
Experiments | 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 |
Introduction | 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. |
Model | (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. |
Evaluation | Both our systems (Model-1 and Model-2) beat the baseline phrase-based system with a BLEU point difference of 4.30 and 2.75 respectively. |
Evaluation | The difference of 2.35 BLEU points between M1 and Pbl indicates that transliteration is useful for more than only translating OOV words for language pairs like Hindi-Urdu. |
Final Results | BLEU point improvement and combined with all the heuristics (M2H123) gives an overall gain of 1.95 BLEU points and is close to our best results (M1H12). |
Background | For the phrase-based system, it yields over 0.6 BLEU point gains just after the 3rd iteration on all the data sets. |
Background | Also as shown in Table 1, over 0.7 BLEU point gains are obtained on the phrase-based system after 10 iterations. |
Background | The largest BLEU improvement on the phrase-based system is over 1 BLEU point in most cases. |
Experimental Setup and Results | While N0un+Adj transformations give us an increase of 2.73 BLEU points , Verbs improve the result by only 0.8 points and improvement with Adverbs is even lower. |
Related Work | (2007) have integrated more syntax in a factored translation approach by using CCG su-pertags as a separate factor and have reported a 0.46 BLEU point improvement in Dutch-to-English translations. |
Related Work | In the context of reordering, one recent work (Xu et al., 2009), was able to get an improvement of 0.6 BLEU points by using source syntactic analysis and a constituent reordering scheme like ours for English-to-Turkish translation, but without using any morphology. |