Semantic parsing is the task of converting natural language utterances into machine interpretable meaning representations which can be executed against a real-world environment such as a database. Scaling semantic parsing to arbitrary domains faces two interrelated challenges: obtaining broad coverage training data effectively and cheaply; and developing a model that generalizes to compositional utterances and complex intentions. We address these challenges with a framework which allows to elicit training data from a domain ontology and bootstrap a neural parser which recursively builds derivations of logical forms. In our framework meaning representations are described by sequences of natural language templates, where each template corresponds to a decomposed fragment of the underlying meaning representation. Although artificial, templates can be understood and paraphrased by humans to create natural utterances, resulting in parallel triples of utterances, meaning representations, and their decompositions. These allow us to train a neural semantic parser which learns to compose rules in deriving meaning representations. We crowdsource training data on six domains, covering both single-turn utterances which exhibit rich compositionality, and sequential utterances where a complex task is procedurally performed in steps. We then develop neural semantic parsers which perform such compositional tasks. In general, our approach allows to deploy neural semantic parsers quickly and cheaply from a given domain ontology.
In reaction to these problems in 1970s, the focus of semantic parsing research shifted from rule-based method to empirical or statistical methods, where data and machine learning plays an important role. Statistical semantic parsers typically consist of three key components: a grammar, a trainable model, and a parsing algorithm. The grammar defines the space of derivations from utterances to meaning representations, and the model together with the parsing algorithm find the most likely derivation. An example of early statistical semantic parser is the system @cite_31 based on inductive logic programming (ILP). The system uses ILP to learn control rules for a shift-reduce parser. To train and evaluate their system, zelle1996learning created the dataset which contains 880 queries to a US geography database. These queries are paired with annotated meaning representations in Prolog.
@cite_31
2163274265
This paper presents recent work using the CHILL parser acquisition system to automate the construction of a natural-language interface for database queries. CHILL treats parser acquisition as the learning of search-control rules within a logic program representing a shift-reduce parser and uses techniques from Inductive Logic Programming to learn relational control knowledge. Starting with a general framework for constructing a suitable logical form, CHILL is able to train on a corpus comprising sentences paired with database queries and induce parsers that map subsequent sentences directly into executable queries. Experimental results with a complete database-query application for U.S. geography show that CHILL is able to learn parsers that outperform a preexisting, hand-crafted counterpart. These results demonstrate the ability of a corpus-based system to produce more than purely syntactic representations. They also provide direct evidence of the utility of an empirical approach at the level of a complete natural language application.