Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simple is Better! Lightweight Data Augmentation for Low Resource Slot
  Filling and Intent Classification   Neural-based models have achieved outstanding performance on slot filling and
intent classification, when fairly large in-domain training data are available.
However, as new domains are frequently added, creating sizeable data is
expensive. We show that lightweight augmentation, a set of augmentation methods
involving word span and sentence level operations, alleviates data scarcity
problems. Our experiments on limited data settings show that lightweight
augmentation yields significant performance improvement on slot filling on the
ATIS and SNIPS datasets, and achieves competitive performance with respect to
more complex, state-of-the-art, augmentation approaches. Furthermore,
lightweight augmentation is also beneficial when combined with pre-trained
LM-based models, as it improves BERT-based joint intent and slot filling
models.

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The categories of this paper are: hep-ph
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-ex
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The categories of this paper are: math
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: quant-ph
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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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: gr-qc