Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Large Margin Neural Language Model   We propose a large margin criterion for training neural language models.
Conventionally, neural language models are trained by minimizing perplexity
(PPL) on grammatical sentences. However, we demonstrate that PPL may not be the
best metric to optimize in some tasks, and further propose a large margin
formulation. The proposed method aims to enlarge the margin between the "good"
and "bad" sentences in a task-specific sense. It is trained end-to-end and can
be widely applied to tasks that involve re-scoring of generated text. Compared
with minimum-PPL training, our method gains up to 1.1 WER reduction for speech
recognition and 1.0 BLEU increase for machine translation.

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The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ