| Introduction* | We can’t get Chinese-English bilingual pages when the input is a Chinese query. |
| Statistical Transliteration Model | We use syllables as translation units to build a statistical Chinese-English backward transliteration model in our system. |
| Statistical Transliteration Model | Based on the above alignment method, we can get our statistical Chinese-English backward transliteration model as, |
| Statistical Transliteration Model | Chinese-English backward transliteration has some differences from traditional translation. |
| Conclusions and Future Work | Our string-to-dependency system generates 80% fewer rules, and achieves 1.48 point improvement in BLEU and 2.53 point improvement in TER on the decoding output on the NIST 04 Chinese-English evaluation set. |
| Experiments | We used part of the NIST 2006 Chinese-English large track data as well as some LDC corpora collected for the DARPA GALE program (LDC2005E83, LDC2006E34 and LDC2006G05) as our bilingual training data. |
| Introduction | For example, Chiang (2007) showed that the Hiero system achieved about 1 to 3 point improvement in BLEU on the NIST 03/04/05 Chinese-English evaluation sets compared to a start-of-the-art phrasal system. |
| Introduction | Our string-to-dependency decoder shows 1.48 point improvement in BLEU and 2.53 point improvement in TER on the NIST 04 Chinese-English MT evaluation set. |
| Conclusions | Our experimental results on IWSLT Chinese-English corpus have demonstrated consistent and significant improvement over the widely used word alignment matrix based extraction method. |
| Experimental Results | We do experiments on IWSLT (Paul, 2006) 2006 Chinese-English corpus. |
| Experimental Results | The training corpus consists of 40K Chinese-English parallel sentences in travel domain with to- |
| Abstract | Experimental results on the NIST MT-2005 Chinese-English translation task show that our method statistically significantly outperforms the baseline systems. |
| Conclusions and Future Work | The experimental results on the NIST MT-2005 Chinese-English translation task demonstrate the effectiveness of the proposed model. |
| Introduction | Experiment results on the NIST MT-2005 Chinese-English translation task show that our method significantly outperforms Moses (Koehn et al., 2007), a state-of-the-art phrase-based SMT system, and other linguistically syntax-based methods, such as SCFG-based and STSG-based methods (Zhang et al., 2007). |