Group sparse CNNs for question classification with answer sets
Mingbo Ma, Liang Huang, et al.
ACL 2017
We introduce a neural semantic parser which is interpretable and scalable. Our model converts natural language utterances to intermediate, domain-general natural language representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We achieve the state of the art on SPADES and GRAPHQUESTIONS and obtain competitive results on GEO-QUERY and WEBQUESTIONS. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones.
Mingbo Ma, Liang Huang, et al.
ACL 2017
Armando Solar-Lezama, Liviu Tancau, et al.
ASPLOS 2006
Mo Yu, Wenpeng Yin, et al.
ACL 2017
Preksha Nema, Mitesh M. Khapra, et al.
ACL 2017