  The recent prevalence of pretrained language models (PLMs) has dramatically
shifted the paradigm of semantic parsing, where the mapping from natural
language utterances to structured logical forms is now formulated as a Seq2Seq
task. Despite the promising performance, previous PLM-based approaches often
suffer from hallucination problems due to their negligence of the structural
information contained in the sentence, which essentially constitutes the key
semantics of the logical forms. Furthermore, most works treat PLM as a black
box in which the generation process of the target logical form is hidden
beneath the decoder modules, which greatly hinders the model's intrinsic
interpretability. To address these two issues, we propose to incorporate the
current PLMs with a hierarchical decoder network. By taking the first-principle
structures as the semantic anchors, we propose two novel intermediate
supervision tasks, namely Semantic Anchor Extraction and Semantic Anchor
Alignment, for training the hierarchical decoders and probing the model
intermediate representations in a self-adaptive manner alongside the
fine-tuning process. We conduct intensive experiments on several semantic
parsing benchmarks and demonstrate that our approach can consistently
outperform the baselines. More importantly, by analyzing the intermediate
representations of the hierarchical decoders, our approach also makes a huge
step toward the intrinsic interpretability of PLMs in the domain of semantic
parsing.
