Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Audio Captioning using Pre-Trained Large-Scale Language Model Guided by
  Audio-based Similar Caption Retrieval   The goal of audio captioning is to translate input audio into its description
using natural language. One of the problems in audio captioning is the lack of
training data due to the difficulty in collecting audio-caption pairs by
crawling the web. In this study, to overcome this problem, we propose to use a
pre-trained large-scale language model. Since an audio input cannot be directly
inputted into such a language model, we utilize guidance captions retrieved
from a training dataset based on similarities that may exist in different
audio. Then, the caption of the audio input is generated by using a pre-trained
language model while referring to the guidance captions. Experimental results
show that (i) the proposed method has succeeded to use a pre-trained language
model for audio captioning, and (ii) the oracle performance of the pre-trained
model-based caption generator was clearly better than that of the conventional
method trained from scratch.

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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
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The categories of this paper are: stat
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The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-th
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The categories of this paper are: quant-ph