Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identifying Computer-Translated Paragraphs using Coherence Features   We have developed a method for extracting the coherence features from a
paragraph by matching similar words in its sentences. We conducted an
experiment with a parallel German corpus containing 2000 human-created and 2000
machine-translated paragraphs. The result showed that our method achieved the
best performance (accuracy = 72.3%, equal error rate = 29.8%) when it is
compared with previous methods on various computer-generated text including
translation and paper generation (best accuracy = 67.9%, equal error rate =
32.0%). Experiments on Dutch, another rich resource language, and a low
resource one (Japanese) attained similar performances. It demonstrated the
efficiency of the coherence features at distinguishing computer-translated from
human-created paragraphs on diverse languages.

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The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci