Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Kernelized Bayesian Softmax for Text Generation   Neural models for text generation require a softmax layer with proper token
embeddings during the decoding phase. Most existing approaches adopt single
point embedding for each token. However, a word may have multiple senses
according to different context, some of which might be distinct. In this paper,
we propose KerBS, a novel approach for learning better embeddings for text
generation. KerBS embodies two advantages: (a) it employs a Bayesian
composition of embeddings for words with multiple senses; (b) it is adaptive to
semantic variances of words and robust to rare sentence context by imposing
learned kernels to capture the closeness of words (senses) in the embedding
space. Empirical studies show that KerBS significantly boosts the performance
of several text generation tasks.

13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
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The categories of this paper are: math
5
The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex