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.

使用 SLFNet 提出一种新型神经网络，将语义解析任务中的依赖句法信息作为先验知识加入模型中，捕捉上下文信息和单词之间的长程交互，并构建语义概率图以获取预测变量之间的局部依赖关系，从而通过多头 SLF Attention 机制从自然语言指令中综合生成 SLFs。实验证明，SLFNet 在 ChineseQCI-TS 和 Okapi 数据集上达到了最先进的性能，并在 ATIS 数据集上获得了有竞争力的结果。