Translation Shared Task on Statistical Machine Translation: A Comparison of the Systems Output

Translation Shared Task on Statistical Machine Translation: A Comparison of the Systems Output

The ACL Workshop on Statistical Machine Translation proposed a translation shared task focused on European language pairs. Participants used their systems to translate into the target language a test set of unseen sentences in the source language. Involved languages were French, English, Spanish, German, Czech and Hungarian. The goal of this work is to quantitatively compare the translations ge...

Shared Task: Statistical Machine Translation between European Languages

The ACL-2005 Workshop on Parallel Texts hosted a shared task on building statistical machine translation systems for four European language pairs: French–English, German–English, Spanish–English, and Finnish–English. Eleven groups participated in the event. This paper describes the goals, the task definition and resources, as well as results and some analysis. Statistical machine translation is...

Error Analysis of Statistical Machine Translation Output

Evaluation of automatic translation output is a difficult task. Several performance measures like Word Error Rate, Position Independent Word Error Rate and the BLEU and NIST scores are widely use and provide a useful tool for comparing different systems and to evaluate improvements within a system. However the interpretation of all of these measures is not at all clear, and the identification o...

A Shared Task on Bandit Learning for Machine Translation

We introduce and describe the results of a novel shared task on bandit learning for machine translation. The task was organized jointly by Amazon and Heidelberg University for the first time at the Second Conference on Machine Translation (WMT 2017). The goal of the task is to encourage research on learning machine translation from weak user feedback instead of human references or post-edits. O...

The POSTECH's Statistical Machine Translation Systems for the NTCIR-8 Patent Translation Task