  Building natural language interfaces typically uses a semantic parser to
parse the user's natural language and convert it into structured
\textbf{S}emantic \textbf{L}ogic \textbf{F}orms (SLFs). The mainstream approach
is to adopt a sequence-to-sequence framework, which requires that natural
language commands and SLFs must be represented serially. Since a single natural
language may have multiple SLFs or multiple natural language commands may have
the same SLF, training a sequence-to-sequence model is sensitive to the choice
among them, a phenomenon recorded as "order matters". To solve this problem, we
propose a novel neural network, SLFNet, which firstly incorporates dependent
syntactic information as prior knowledge and can capture the long-range
interactions between contextual information and words. Secondly construct
semantic probability graphs to obtain local dependencies between predictor
variables. Finally we propose the Multi-Head SLF Attention mechanism to
synthesize SLFs from natural language commands based on Sequence-to-Slots.
Experiments show that SLFNet achieves state-of-the-art performance on the
ChineseQCI-TS and Okapi datasets, and competitive performance on the ATIS
dataset.
