We introduce a novel semi-automated metric, MEANT, that assesses translation utility by matching semantic role fillers, producing scores that correlate with human judgment as well as HTER but at much lower labor cost. As machine translation systems improve in lexical choice and fluency, the shortcomings of widespread n-gram based, fluency-oriented MT evaluation metrics such as BLEU, which fail to properly evaluate adequacy, become more apparent.
In evaluating the output of language technology applications—MT, natural language generation, summarisation—automatic evaluation techniques generally conﬂate measurement of faithfulness to source content with ﬂuency of the resulting text. In this paper we develop an automatic evaluation metric to estimate ﬂuency alone, by examining the use of parser outputs as metrics, and show that they correlate with human judgements of generated text ﬂuency.
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