Papers with machine interpretable meaning representations
Dependency-based Hybrid Trees for Semantic Parsing (D18-1)
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| Challenge: | Existing models for semantic parsing focus on structure-based models, but none deal with dependency information. |
| Approach: | They propose a dependency-based hybrid tree model which converts natural language utterances into machine interpretable meaning representations. |
| Outcome: | The proposed model achieves state-of-the-art performance across eight languages and is highly tractable inferenceable. |
Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs (D19-1)
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| Challenge: | Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations. |
| Approach: | They propose to instill an inductive bias in the parser to help it distinguish between spurious and correct programs. |
| Outcome: | The proposed model is highly tractable on WikiTableQuestions and WikiSQL datasets. |