Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Translation Evaluation Resources and Methods: A Survey   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.
  We hope this work will be helpful for MT researchers to easily pick up some
metrics that are best suitable for their specific MT model development, and
help MT evaluation researchers to get a general clue of how MT evaluation
research developed. Furthermore, hopefully, this work can also shine some light
on other evaluation tasks, except for translation, of NLP fields.

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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: physics
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: econ
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas