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Conversational semantic parsing over tables requires knowledge acquiring andreasoning abilities, which have not been well explored by currentstate-of-the-art approaches. Motivated by this fact, we propose aknowledge-aware semantic parser to improve parsing performance by integratingvarious types of knowledge. In this paper, we consider three types ofknowledge, including grammar knowledge, expert knowledge, and external resourceknowledge. First, grammar knowledge empowers the model to effectively replicatepreviously generated logical form, which effectively handles the co-referenceand ellipsis phenomena in conversation Second, based on expert knowledge, wepropose a decomposable model, which is more controllable compared withtraditional end-to-end models that put all the burdens of learning ontrial-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 toimprove the representation of question and table for a better semanticunderstanding. We conduct experiments on the SequentialQA dataset. Results showthat our knowledge-aware model outperforms the state-of-the-art approaches.Incremental experimental results also prove the usefulness of variousknowledge. Further analysis shows that our approach has the ability to derivethe meaning representation of a context-dependent utterance by leveragingpreviously generated outcomes.

Knowledge-Aware Conversational Semantic Parsing Over Web Tables