CE_LD CE_SD testset ( NIST ) ’06 ’08 ’06 ’08

Table 4: Results (BLEU%) of Chinese—to—English large data (CE_LD) and small data (CE_SD) NIST task by applying one feature.

Table 6: Results (BLEU%) of using simple features based on context on small data NIST task.

The first one is the large data condition, based on training data for the NIST 2 2009 evaluation Chinese-to-English track.

We first created a development set which used mainly data from the NIST 2005 test set, and also some balanced-genre web-text from the NIST training material.

Evaluation was performed on the NIST 2006 and 2008 test sets.

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

2 In this paper, we use the NIST definition of BLEU where the effective reference length is the length of the shortest reference translation.

The data set used for weight training in boosting-based system combination comes from NIST MTO3 evaluation set.

The test sets are the NIST evaluation sets of MTO4, MTOS and MTO6.

All the systems are evaluated on three NIST MT evaluation test sets.

Additionally, NIST score (Dod-dington, 2002) and METEOR (Banerjee and La-vie, 2005) are also used to check the consistency of experimental results.

BLEU 0.4029 0.3146 NIST 7.0419 8.8462 METEOR 0.5785 0.5335

Both SMP and ESSP outperform baseline consistently in BLEU, NIST and METEOR.

We train and test linguistic quality models on consecutive years of NIST evaluation data in order to show the generality of results.

Automatic evaluation will make testing easier during system development and enable reporting results obtained outside of the cycles of NIST evaluation.

quality and none have been validated on data from NIST evaluations.

We evaluate the predictive power of these linguistic quality metrics by training and testing models on consecutive years of NIST evaluations (data described

In both DUC 2006 and DUC 2007, ten NIST assessors wrote summaries for the various inputs.

We only report results on the input level, as we are interested in distinguishing between the quality of the summaries, not the NIST assessors’ writing skills.

For the error detection task, we use the best translation hypotheses of NIST MT-02/05/03 generated by MOSES as our training, development, and test corpus respectively.

The translation task is on the official NIST Chinese-to-English evaluation data.

For minimum error rate tuning (Och, 2003), we use NIST MT-02 as the development set for the translation task.

In order to calculate word posterior probabilities, we generate 10,000 best lists for NIST MT-02/03/05 respectively.