Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Lattention: Lattice-attention in ASR rescoring   Lattices form a compact representation of multiple hypotheses generated from
an automatic speech recognition system and have been shown to improve
performance of downstream tasks like spoken language understanding and speech
translation, compared to using one-best hypothesis. In this work, we look into
the effectiveness of lattice cues for rescoring n-best lists in second-pass. We
encode lattices with a recurrent network and train an attention encoder-decoder
model for n-best rescoring. The rescoring model with attention to lattices
achieves 4-5% relative word error rate reduction over first-pass and 6-8% with
attention to both lattices and acoustic features. We show that rescoring models
with attention to lattices outperform models with attention to n-best
hypotheses. We also study different ways to incorporate lattice weights in the
lattice encoder and demonstrate their importance for n-best rescoring.

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The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int