Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Transformer based Data Augmentation with Subword Units for
  Morphologically Rich Online ASR   Recently Deep Transformer models have proven to be particularly powerful in
language modeling tasks for ASR. Their high complexity, however, makes them
very difficult to apply in the first (single) pass of an online system. Recent
studies showed that a considerable part of the knowledge of neural network
Language Models (LM) can be transferred to traditional n-grams by using neural
text generation based data augmentation. In our paper, we pre-train a GPT-2
Transformer LM on a general text corpus and fine-tune it on our Hungarian
conversational call center ASR task. We show that although data augmentation
with Transformer-generated text works well for isolating languages, it causes a
vocabulary explosion in a morphologically rich language. Therefore, we propose
a new method called subword-based neural text augmentation, where we retokenize
the generated text into statistically derived subwords. We compare Morfessor
and BPE statistical subword tokenizers and show that both methods can
significantly improve the WER while greatly reducing vocabulary size and memory
requirements. Finally, we also demonstrate that subword-based neural text
augmentation outperforms the word-based approach not only in terms of overall
WER but also in recognition of OOV words.

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The categories of this paper are: supr-con
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The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: econ
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: q-bio
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The categories of this paper are: math
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: adap-org
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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys