@InProceedings{cheng-lapata:2018:K18-1,
  author    = {Cheng, Jianpeng  and  Lapata, Mirella},
  title     = {Weakly-Supervised Neural Semantic Parsing with a Generative Ranker},
  booktitle = {Proceedings of the 22nd Conference on Computational Natural Language Learning},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {356--367},
  abstract  = {Weakly-supervised semantic parsers are trained on utterance-denotation pairs, treating logical forms as latent. The task is challenging due to the large search space and spuriousness of logical forms. In this paper we introduce a neural parser-ranker system for weakly-supervised semantic parsing. The parser generates candidate tree-structured logical forms from utterances using clues of denotations. These candidates are then ranked based on two criterion: their likelihood of executing to the correct denotation, and their agreement with the utterance semantics. We present a scheduled training procedure to balance the contribution of the two objectives. Furthermore, we propose to use a neurally encoded lexicon to inject prior domain knowledge to the model. Experiments on three Freebase datasets demonstrate the effectiveness of our semantic parser, achieving results within the state-of-the-art range.},
  url       = {http://www.aclweb.org/anthology/K18-1035}
}