Multi-Target Semantic Parsing with Collaborative Deliberation Network

Xiang Li, Fangyu Lei, Shizhu He, Kang Liu, Jun Zhao

Abstract: Semantic parsing aims at mapping natural language utterances into machine-interpretable meaning representations, facilitating user accesses to knowledge bases. However, knowledge in real-world scenarios is often duplicated in multiple storages and different representations. Although researchers have made great success by improving neural semantic parsers, existing works can only handle a specific kind of meaning representation, ie, the single-target semantic parsing. In this paper, we introduce a multi-target semantic parsing model based on a collaborative deliberation network, which can not only decode multiple meaning representations simultaneously but also allow meaning representations to make use of information from each other while decoding. Experiments show that the proposed model improves the EM accuracy of four MRs averagely by 2.48% to 5.05% on three public datasets.