Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SMT vs NMT: A Comparison over Hindi & Bengali Simple Sentences   In the present article, we identified the qualitative differences between
Statistical Machine Translation (SMT) and Neural Machine Translation (NMT)
outputs. We have tried to answer two important questions: 1. Does NMT perform
equivalently well with respect to SMT and 2. Does it add extra flavor in
improving the quality of MT output by employing simple sentences as training
units. In order to obtain insights, we have developed three core models viz.,
SMT model based on Moses toolkit, followed by character and word level NMT
models. All of the systems use English-Hindi and English-Bengali language pairs
containing simple sentences as well as sentences of other complexity. In order
to preserve the translations semantics with respect to the target words of a
sentence, we have employed soft-attention into our word level NMT model. We
have further evaluated all the systems with respect to the scenarios where they
succeed and fail. Finally, the quality of translation has been validated using
BLEU and TER metrics along with manual parameters like fluency, adequacy etc.
We observed that NMT outperforms SMT in case of simple sentences whereas SMT
outperforms in case of all types of sentence.

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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ph
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
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The categories of this paper are: acc-phys
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: nlin