SLFNet: Generating Semantic Logic Forms from Natural Language Using Semantic Probability Graphs

• URL: http://arxiv.org/abs/2403.19936v1
• Date: Fri, 29 Mar 2024 02:42:39 GMT
• Title: SLFNet: Generating Semantic Logic Forms from Natural Language Using Semantic Probability Graphs
• Authors: Hao Wu, Fan Xu,
• Abstract summary: Building natural language interfaces typically uses a semanticSlot to parse the user's natural language and convert it into structured textbfSemantic textbfLogic textbfForms (SLFs) We propose a novel neural network, SLFNet, which incorporates dependent syntactic information as prior knowledge and can capture the long-range interactions between contextual information and words. Experiments show that SLFNet achieves state-of-the-art performance on the ChineseQCI-TS and Okapi datasets, and competitive performance on the ATIS dataset
• Score: 6.689539418123863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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