Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data Augmentation for Sign Language Gloss Translation   Sign language translation (SLT) is often decomposed into video-to-gloss
recognition and gloss-to-text translation, where a gloss is a sequence of
transcribed spoken-language words in the order in which they are signed. We
focus here on gloss-to-text translation, which we treat as a low-resource
neural machine translation (NMT) problem. However, unlike traditional
low-resource NMT, gloss-to-text translation differs because gloss-text pairs
often have a higher lexical overlap and lower syntactic overlap than pairs of
spoken languages. We exploit this lexical overlap and handle syntactic
divergence by proposing two rule-based heuristics that generate pseudo-parallel
gloss-text pairs from monolingual spoken language text. By pre-training on the
thus obtained synthetic data, we improve translation from American Sign
Language (ASL) to English and German Sign Language (DGS) to German by up to
3.14 and 2.20 BLEU, respectively.

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The categories of this paper are: math
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg