Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Jointly Learning to Align and Convert Graphemes to Phonemes with Neural
  Attention Models   We propose an attention-enabled encoder-decoder model for the problem of
grapheme-to-phoneme conversion. Most previous work has tackled the problem via
joint sequence models that require explicit alignments for training. In
contrast, the attention-enabled encoder-decoder model allows for jointly
learning to align and convert characters to phonemes. We explore different
types of attention models, including global and local attention, and our best
models achieve state-of-the-art results on three standard data sets (CMUDict,
Pronlex, and NetTalk).

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The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
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The categories of this paper are: funct-an
2
The categories of this paper are: math
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The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an