Conversational semantic parsing over tables requires knowledge acquiring and
reasoning abilities, which have not been well explored by current
state-of-the-art approaches. Motivated by this fact, we propose a
knowledge-aware semantic parser to improve parsing performance by integrating
various types of knowledge. In this paper, we consider three types of
knowledge, including grammar knowledge, expert knowledge, and external resource
knowledge. First, grammar knowledge empowers the model to effectively replicate
previously generated logical form, which effectively handles the co-reference
and ellipsis phenomena in conversation Second, based on expert knowledge, we
propose a decomposable model, which is more controllable compared with
traditional end-to-end models that put all the burdens of learning on
trial-and-error in an end-to-end way. Third, external resource knowledge, i.e.,
provided by a pre-trained language model or an entity typing model, is used to
improve the representation of question and table for a better semantic
understanding. We conduct experiments on the SequentialQA dataset. Results show
that our knowledge-aware model outperforms the state-of-the-art approaches.
Incremental experimental results also prove the usefulness of various
knowledge. Further analysis shows that our approach has the ability to derive
the meaning representation of a context-dependent utterance by leveraging
previously generated outcomes.

本文提出一种知识感知型的语义解析模型，通过整合不同类型的知识，包括语法知识、专家知识和外部资源知识，提高了对表格的对话语义解析性能。实验结果表明，该模型优于现有最先进的方法，还证明了各种知识的有效性。