Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with
  Self-training   Natural language generation from structured data mainly focuses on
surface-level descriptions, suffering from uncontrollable content selection and
low fidelity. Previous works leverage logical forms to facilitate logical
knowledge-conditioned text generation. Though achieving remarkable progress,
they are data-hungry, which makes the adoption for real-world applications
challenging with limited data. To this end, this paper proposes a unified
framework for logical knowledge-conditioned text generation in the few-shot
setting. With only a few seeds logical forms (e.g., 20/100 shot), our approach
leverages self-training and samples pseudo logical forms based on content and
structure consistency. Experimental results demonstrate that our approach can
obtain better few-shot performance than baselines.

22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat