  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.
