SLFNet: Generating Semantic Logic Forms from Natural Language Using Semantic Probability Graphs
<|reference_start|>SLFNet: Generating Semantic Logic Forms from Natural Language Using Semantic Probability Graphs: 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.<|reference_end|>
@article{wu2024slfnet:,
    title={SLFNet: Generating Semantic Logic Forms from Natural Language Using
  Semantic Probability Graphs},
    author={Hao Wu, Fan Xu},
    journal={arXiv preprint arXiv:2403.19936},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19936},
    primaryClass={cs.CL}
}