Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: How Robust Are Character-Based Word Embeddings in Tagging and MT Against
  Wrod Scramlbing or Randdm Nouse?   This paper investigates the robustness of NLP against perturbed word forms.
While neural approaches can achieve (almost) human-like accuracy for certain
tasks and conditions, they often are sensitive to small changes in the input
such as non-canonical input (e.g., typos). Yet both stability and robustness
are desired properties in applications involving user-generated content, and
the more as humans easily cope with such noisy or adversary conditions. In this
paper, we study the impact of noisy input. We consider different noise
distributions (one type of noise, combination of noise types) and mismatched
noise distributions for training and testing. Moreover, we empirically evaluate
the robustness of different models (convolutional neural networks, recurrent
neural networks, non-neural models), different basic units (characters, byte
pair encoding units), and different NLP tasks (morphological tagging, machine
translation).

16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas