Papers with machine executable meaning representations

1 papers
Reranking for Neural Semantic Parsing (P19-1)

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Challenge: Semantic parsing is the task of transducing natural language utterances into machine executable meaning representations (e.g., Python code).
Approach: They propose to rerank an n-best list of predicted MRs and use features to fix observed problems with baseline models to improve parser performance.
Outcome: The proposed method outperforms the best published neural parser on four datasets and improves the baseline parsing performance by 5.7% and 2.9%.

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