Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Leveraging translations for speech transcription in low-resource
  settings   Recently proposed data collection frameworks for endangered language
documentation aim not only to collect speech in the language of interest, but
also to collect translations into a high-resource language that will render the
collected resource interpretable. We focus on this scenario and explore whether
we can improve transcription quality under these extremely low-resource
settings with the assistance of text translations. We present a neural
multi-source model and evaluate several variations of it on three low-resource
datasets. We find that our multi-source model with shared attention outperforms
the baselines, reducing transcription character error rate by up to 12.3%.

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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: eess
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The categories of this paper are: solv-int
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
3
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: astro-ph
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The categories of this paper are: econ
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The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph