Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Controlling Utterance Length in NMT-based Word Segmentation with
  Attention   One of the basic tasks of computational language documentation (CLD) is to
identify word boundaries in an unsegmented phonemic stream. While several
unsupervised monolingual word segmentation algorithms exist in the literature,
they are challenged in real-world CLD settings by the small amount of available
data. A possible remedy is to take advantage of glosses or translation in a
foreign, well-resourced, language, which often exist for such data. In this
paper, we explore and compare ways to exploit neural machine translation models
to perform unsupervised boundary detection with bilingual information, notably
introducing a new loss function for jointly learning alignment and
segmentation. We experiment with an actual under-resourced language, Mboshi,
and show that these techniques can effectively control the output segmentation
length.

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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: physics
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-bio
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph