Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information. |
| Approach: | They propose to use syntactic graph to represent three types of syntaktic information . they then employ a graph-to-sequence model to encode the syntastic graph and decode a logical form . |
| Outcome: | The proposed model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. |
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| Challenge: | Abstract Meaning Representation (AMR) parsing is a broad-coverage semantic formalism that encodes the meaning of a sentence as a rooted, directed, labeled graph. |
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| Challenge: | Semantic parsing is the task of mapping natural language to machine interpretable meaning representations. |
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Semantics as a Foreign Language (D18-1)
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| Challenge: | (2017): Syntactic grammars capture propositions, but graph-based representations aim to capture a wider notion of propositions. |
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