How do we build a semantic parser in a new domain starting with zero training ex-amples? We introduce a new methodol-ogy for this setting: First, we use a simple grammar to generate logical forms paired with canonical utterances. The logical forms are meant to cover the desired set of compositional operators, and the canon-ical utterances are meant to capture the meaning of the logical forms (although clumsily). We then use crowdsourcing to paraphrase these canonical utterances into natural utterances. The resulting data is used to train the semantic parser. We fur-ther study the role of compositionality in the resulting paraphrases. Finally, we test our methodology on seven domains and show that we can build an adequate se-mantic parser in...
While there has been significant recent work on learning semantic parsers for specific task/ domains...
We present an approach to training a joint syntactic and semantic parser that com-bines syntactic tr...
We introduce a learning semantic parser, SCISSOR, that maps natural-language sentences to a detailed...
How do we build a semantic parser in a new domain starting with zero training ex-amples? We introduc...
Semantic parsing is an important NLP problem, particularly for voice assistants such as Alexa and Go...
A central challenge in semantic parsing is handling the myriad ways in which knowl-edge base predica...
A central challenge in semantic parsing is handling the myriad ways in which knowl-edge base predica...
In this thesis, we investigate an approach for grammar induction that relies on se-mantics to drive ...
Semantic parsing is the task of mapping a natural language sentence into a complete, formal meaning ...
This paper presents an empirical method for mapping speech input to shallow semantic representation....
Computational systems that learn to transform natural-language sentences into semantic representatio...
Research in semantic parsing has focused on developing computational systems capable of simultaneous...
Semantic parsing is the task of translating natural language utterances onto machine-interpretable p...
We present a method for training a semantic parser using only a knowledge base and an unlabeled text...
For building question answering systems and natural lan-guage interfaces, semantic parsing has emerg...
While there has been significant recent work on learning semantic parsers for specific task/ domains...
We present an approach to training a joint syntactic and semantic parser that com-bines syntactic tr...
We introduce a learning semantic parser, SCISSOR, that maps natural-language sentences to a detailed...
How do we build a semantic parser in a new domain starting with zero training ex-amples? We introduc...
Semantic parsing is an important NLP problem, particularly for voice assistants such as Alexa and Go...
A central challenge in semantic parsing is handling the myriad ways in which knowl-edge base predica...
A central challenge in semantic parsing is handling the myriad ways in which knowl-edge base predica...
In this thesis, we investigate an approach for grammar induction that relies on se-mantics to drive ...
Semantic parsing is the task of mapping a natural language sentence into a complete, formal meaning ...
This paper presents an empirical method for mapping speech input to shallow semantic representation....
Computational systems that learn to transform natural-language sentences into semantic representatio...
Research in semantic parsing has focused on developing computational systems capable of simultaneous...
Semantic parsing is the task of translating natural language utterances onto machine-interpretable p...
We present a method for training a semantic parser using only a knowledge base and an unlabeled text...
For building question answering systems and natural lan-guage interfaces, semantic parsing has emerg...
While there has been significant recent work on learning semantic parsers for specific task/ domains...
We present an approach to training a joint syntactic and semantic parser that com-bines syntactic tr...
We introduce a learning semantic parser, SCISSOR, that maps natural-language sentences to a detailed...