Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Skill Transfer from Supervised Language Tasks to Reading
  Comprehension   Reading comprehension is a challenging task in natural language processing
and requires a set of skills to be solved. While current approaches focus on
solving the task as a whole, in this paper, we propose to use a neural network
`skill' transfer approach. We transfer knowledge from several lower-level
language tasks (skills) including textual entailment, named entity recognition,
paraphrase detection and question type classification into the reading
comprehension model.
  We conduct an empirical evaluation and show that transferring language skill
knowledge leads to significant improvements for the task with much fewer steps
compared to the baseline model. We also show that the skill transfer approach
is effective even with small amounts of training data. Another finding of this
work is that using token-wise deep label supervision for text classification
improves the performance of transfer learning.

16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat