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. |
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| Challenge: | Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations. |
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| Challenge: | training semantic parsers from weak supervision complicates training in two ways . spurious programs that accidentally lead to a correct denotation add noise to training . |
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Andrew Drozdov, Jiawei Zhou, Radu Florian, Andrew McCallum, Tahira Naseem, Yoon Kim, Ramón Astudillo
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| Challenge: | Natural language interfaces are often ambiguous, vague, or underspecified, giving rise to multiple valid interpretations. |
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| Challenge: | Existing methods for training semantic parsers from only (utterance, denotation) supervision are challenging. |
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Weakly Supervised Semantic Parsing with Execution-based Spurious Program Filtering (2023.emnlp-main)
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| Challenge: | Existing methods to train a semantic parser from weak supervision focus on exploiting similarities between examples based on domain-specific knowledge. |
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Learning from Executions for Semantic Parsing (2021.naacl-main)
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| Challenge: | Semantic parsing aims at translating natural language (NL) utterances onto machine-interpretable programs. |
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