Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pre-Learning Environment Representations for Data-Efficient Neural
  Instruction Following   We consider the problem of learning to map from natural language instructions
to state transitions (actions) in a data-efficient manner. Our method takes
inspiration from the idea that it should be easier to ground language to
concepts that have already been formed through pre-linguistic observation. We
augment a baseline instruction-following learner with an initial
environment-learning phase that uses observations of language-free state
transitions to induce a suitable latent representation of actions before
processing the instruction-following training data. We show that mapping to
pre-learned representations substantially improves performance over systems
whose representations are learned from limited instructional data alone.

5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math