Lifecycle of neural semantic parsing
Humans are born with the ability to learn to perceive, comprehend and communicate with language. Computing machines, on the other hand, only understand programming languages. To bridge the gap between humans and computers, deep semantic parsers convert natural language utterances into machine understandable logical forms. The technique has a wide range of applications ranging from spoken dialogue systems and natural language interfaces. This thesis focuses on neural network based semantic parsing. Traditional semantic parsers function with a domain specific grammar that pairs utterances and logical forms, and parse with a CKY like algorithm in polynomial time. Recent advances in neural semantic parsing reformulate the task as a sequenceto sequence learning problem. Neural semantic parsers parse a sentence in linear time, and reduce the need for domain specific assumptions, grammar learning, and extensive feature engineering. But this modeling flexibility comes at a cost since it is no longer possible to interpret how meaning composition is performed, given that logical forms are structured objects (trees or graphs) Such knowledge plays a critical role in understanding modeling limitations so as to build better semantic parsers. Moreover, the sequence to sequence learning problem is fairly unconstrained, both in terms of the possible derivations to consider and in terms of the target logical forms which can be ill formed or unexecutable. The first contribution of this thesis is an improved neural semantic parser, which produces syntactically valid logical forms following a transition system and grammar constrains. The transition system integrates the generation of domain general (i.e. valid tree structures and language specific predicates) and domain specific aspects (i.e. domain specific predicates and entities) in a unified way. The model employs various neural attention mechanisms to handle mismatches between natural language and formal language a central challenge in semantic parsing. Training data to semantic parsers typically consists of utterances paired with logical forms. Another challenge of semantic parsing concerns the annotation of logical forms, which is labor intensive. To write down the correct logical form of an utterance, one not only needs to have expertise in the semantic formalism, but also has to ensure the logical form matches the utterance semantics. We tackle this challenge in two ways. On the one hand, we extend the neural semantic parser to a weakly supervised setting within a parser ranker framework. The weakly supervised setup uses training data of utterance denotation (e.g. question answer) pairs, which are much easier to obtain and therefore allow to scale semantic parsers to complex domains. Our framework