Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data Recombination for Neural Semantic Parsing   Modeling crisp logical regularities is crucial in semantic parsing, making it
difficult for neural models with no task-specific prior knowledge to achieve
good results. In this paper, we introduce data recombination, a novel framework
for injecting such prior knowledge into a model. From the training data, we
induce a high-precision synchronous context-free grammar, which captures
important conditional independence properties commonly found in semantic
parsing. We then train a sequence-to-sequence recurrent network (RNN) model
with a novel attention-based copying mechanism on datapoints sampled from this
grammar, thereby teaching the model about these structural properties. Data
recombination improves the accuracy of our RNN model on three semantic parsing
datasets, leading to new state-of-the-art performance on the standard GeoQuery
dataset for models with comparable supervision.

12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
2
The categories of this paper are: math
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat