Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models
  Robust with Little Cost   State-of-the-art NLP systems represent inputs with word embeddings, but these
are brittle when faced with Out-of-Vocabulary (OOV) words. To address this
issue, we follow the principle of mimick-like models to generate vectors for
unseen words, by learning the behavior of pre-trained embeddings using only the
surface form of words. We present a simple contrastive learning framework,
LOVE, which extends the word representation of an existing pre-trained language
model (such as BERT), and makes it robust to OOV with few additional
parameters. Extensive evaluations demonstrate that our lightweight model
achieves similar or even better performances than prior competitors, both on
original datasets and on corrupted variants. Moreover, it can be used in a
plug-and-play fashion with FastText and BERT, where it significantly improves
their robustness.

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The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
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The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph