Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Logic-Consistency Text Generation from Semantic Parses   Text generation from semantic parses is to generate textual descriptions for
formal representation inputs such as logic forms and SQL queries. This is
challenging due to two reasons: (1) the complex and intensive inner logic with
the data scarcity constraint, (2) the lack of automatic evaluation metrics for
logic consistency. To address these two challenges, this paper first proposes
SNOWBALL, a framework for logic consistent text generation from semantic parses
that employs an iterative training procedure by recursively augmenting the
training set with quality control. Second, we propose a novel automatic metric,
BLEC, for evaluating the logical consistency between the semantic parses and
generated texts. The experimental results on two benchmark datasets, Logic2Text
and Spider, demonstrate the SNOWBALL framework enhances the logic consistency
on both BLEC and human evaluation. Furthermore, our statistical analysis
reveals that BLEC is more logically consistent with human evaluation than
general-purpose automatic metrics including BLEU, ROUGE and, BLEURT. Our data
and code are available at https://github.com/Ciaranshu/relogic.

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The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
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The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con