Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Long-span language modeling for speech recognition   We explore neural language modeling for speech recognition where the context
spans multiple sentences. Rather than encode history beyond the current
sentence using a cache of words or document-level features, we focus our study
on the ability of LSTM and Transformer language models to implicitly learn to
carry over context across sentence boundaries. We introduce a new architecture
that incorporates an attention mechanism into LSTM to combine the benefits of
recurrent and attention architectures. We conduct language modeling and speech
recognition experiments on the publicly available LibriSpeech corpus. We show
that conventional training on a paragraph-level corpus results in significant
reductions in perplexity compared to training on a sentence-level corpus. We
also describe speech recognition experiments using long-span language models in
second-pass re-ranking, and provide insights into the ability of such models to
take advantage of context beyond the current sentence.

2
The categories of this paper are: math
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat