| Abstract | To deal with the high degree of ambiguity present in this setting, we present a generative model that simultaneously segments the text into utterances and maps each utterance to a meaning representation grounded in the world state. |
| Generative Model | Think of the words spanned by a record as constituting an utterance with a meaning representation given by the record and subset of fields chosen. |
| Introduction | Recent work in learning semantics has focused on mapping sentences to meaning representations (e.g., some logical form) given aligned sen-tence/meaning pairs as training data (Ge and Mooney, 2005; Zettlemoyer and Collins, 2005; Zettlemoyer and Collins, 2007; Lu et al., 2008). |
| Introduction | In this less restricted data setting, we must resolve multiple ambiguities: (l) the segmentation of the text into utterances; (2) the identification of relevant facts, i.e., the choice of records and aspects of those records; and (3) the alignment of utterances to facts (facts are the meaning representations of the utterances). |
| Experimental Evaluation | Note the results for SCISSOR, KRISP and LU on GEOQUERY are based on a different meaning representation language, FUNQL, which has been shown to produce lower results (Wong and Mooney, 2007). |
| Introduction | Semantic parsing is the task of mapping a natural language (NL) sentence into a completely formal meaning representation (MR) or logical form. |
| Introduction | A meaning representation language (MRL) is a formal unambiguous language that supports automated inference, such as first-order predicate logic. |