Recently, many BERT based approaches have 001 been proposed for task-oriented dialogue 002 (TOD) task. Despite their impressive perfor-003 mance, the insufficient utilization of deep se-004 mantic information and long-distance context 005 understanding makes it difficult for these meth-006 ods to digest complex dialogue scenarios for 007 they cannot obtain sufficient evidence from dia-008 logue data to support dialogue decision-making. 009 In this work, we propose a novel structured se-010 mantics reinforcement (SSR) method to handle 011 these issues. SSR reorganized the end-to-end 012 TOD structure, which mainly includes two key 013 components: 1. The dialogue symbolic mem-014 ory, which cache the objects mentioned in the 015 dialogue and the structure under the seman-016 tic relationship. 2. semantic projection mod-017 ule, understanding module, based on the pre-018 vious structured results, determines the source 019 of the slot extraction required for the current 020 task. And our approach achieves state-of-the-021 art results on dataset MultiWOZ 2.1, where 022 we acquire a joint goal accuracy beyond 60% 023 and also gains a significant effect on dataset 024 DSTC8. 025
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