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Evaluation of machine translation output is an important but difficult task. Over the last years, a variety of automatic evalua- tion measures have been studied, some of them like Word …...

Current phrase-based statistical machine translation systems process each test sentence in isolation and do not enforce global consis- tency constraints, even though the test data is often internally consistent with …...

Automatic evaluation of Machine Translation (MT) quality is essential to developing high- quality MT systems. Various evaluation met- rics have been proposed, and BLEU is now used as the de …...

We introduce the first fully automatic, fully seman- tic frame based MT evaluation metric, MEANT, that outperforms all other commonly used auto- matic metrics in correlating with human judgment on …...

AbstractWe propose a neural reranking system for named entity recognition (NER), leverages recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output …...