Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Algebraic Recombination for Compositional Generalization   Neural sequence models exhibit limited compositional generalization ability
in semantic parsing tasks. Compositional generalization requires algebraic
recombination, i.e., dynamically recombining structured expressions in a
recursive manner. However, most previous studies mainly concentrate on
recombining lexical units, which is an important but not sufficient part of
algebraic recombination. In this paper, we propose LeAR, an end-to-end neural
model to learn algebraic recombination for compositional generalization. The
key insight is to model the semantic parsing task as a homomorphism between a
latent syntactic algebra and a semantic algebra, thus encouraging algebraic
recombination. Specifically, we learn two modules jointly: a Composer for
producing latent syntax, and an Interpreter for assigning semantic operations.
Experiments on two realistic and comprehensive compositional generalization
benchmarks demonstrate the effectiveness of our model. The source code is
publicly available at https://github.com/microsoft/ContextualSP.

19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph