We introduce the Machine Translation (MT) evaluation survey that contains both manual and automatic evaluation methods. The traditional human evaluation criteria mainly include the intelligibility, fidelity, fluency, adequacy, comprehension, and informativeness. The advanced human assessments include task-oriented measures, post-editing, segment ranking, and extended criteriea, etc. We classify the automatic evaluation methods into two categories, including lexical similarity scenario and linguistic features application. The lexical similarity methods contain edit distance, precision, recall, F-measure, and word order. The linguistic features can be divided into syntactic features and semantic features respectively. The syntactic features include part of speech tag, phrase types and sentence structures, and the semantic features include named entity, synonyms, textual entailment, paraphrase, semantic roles, and language models. The deep learning models for evaluation are very newly proposed. Subsequently, we also introduce the evaluation methods for MT evaluation including different correlation scores, and the recent quality estimation (QE) tasks for MT.
This paper differs from the existing works\cite {GALEprogram2009, EuroMatrixProject2007} from several aspects, by introducing some recent development of MT evaluation measures, the different classifications from manual to automatic evaluation measures, the introduction of recent QE tasks of MT, and the concise construction of the content.
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Funders:
Science Foundation Ireland (SFI) Research Centres Programme (Grant 13/RC/2106), European Regional Development Fund, NWO VICI under Grant No. 277-89- 002 of Netherlands, Research Committee of the University of Macau (Grant No. MYRG2015-00175-FST and MYRG2015-00188- FST), Science and Technology Development Fund of Macau (Grant No. 057/2014/A)
ID Code:
24493
Deposited On:
26 May 2020 12:30 by
Lifeng Han
. Last Modified 20 Sep 2021 13:08