Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LINDA: Unsupervised Learning to Interpolate in Natural Language
  Processing   Despite the success of mixup in data augmentation, its applicability to
natural language processing (NLP) tasks has been limited due to the discrete
and variable-length nature of natural languages. Recent studies have thus
relied on domain-specific heuristics and manually crafted resources, such as
dictionaries, in order to apply mixup in NLP. In this paper, we instead propose
an unsupervised learning approach to text interpolation for the purpose of data
augmentation, to which we refer as "Learning to INterpolate for Data
Augmentation" (LINDA), that does not require any heuristics nor manually
crafted resources but learns to interpolate between any pair of natural
language sentences over a natural language manifold. After empirically
demonstrating the LINDA's interpolation capability, we show that LINDA indeed
allows us to seamlessly apply mixup in NLP and leads to better generalization
in text classification both in-domain and out-of-domain.

15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int