Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised
  Neural Machine Translation   We explore ways of incorporating bilingual dictionaries to enable
semi-supervised neural machine translation. Conventional back-translation
methods have shown success in leveraging target side monolingual data. However,
since the quality of back-translation models is tied to the size of the
available parallel corpora, this could adversely impact the synthetically
generated sentences in a low resource setting. We propose a simple data
augmentation technique to address both this shortcoming. We incorporate widely
available bilingual dictionaries that yield word-by-word translations to
generate synthetic sentences. This automatically expands the vocabulary of the
model while maintaining high quality content. Our method shows an appreciable
improvement in performance over strong baselines.

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The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph
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The categories of this paper are: nlin
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The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: cond-mat
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The categories of this paper are: mtrl-th
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The categories of this paper are: solv-int
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The categories of this paper are: gr-qc
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: q-bio