Cascaded re-ranking modelling of translation hypotheses using extreme learning machines.

In statistical machine translation (SMT), re-ranking of huge amount of randomly generated translation hypotheses is one of the essential components in determining the quality of translation result. In this work, a novel re-ranking modelling framework called cascaded re-ranking modelling (CRM) is proposed by cascading a classification model and a regression model. The proposed CRM effectively and efficiently selects the good but rare hypotheses in order to alleviate simultaneously the issues of translation quality and computational cost. CRM can be partnered with any classifier such as support vector machines (SVM) and extreme learning machine (ELM). Compared to other state-of-the-art methods, experimental results show that CRM partnered with ELM (CRM-ELM) can raise at most 11.6% of translation quality over the popular benchmark Chinese–English corpus (IWSLT 2014) and French–English parallel corpus (WMT 2015) with extremely fast training time for huge corpus. [ABSTRACT FROM AUTHOR]