Papers with Overnight dataset
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data (2020.emnlp-main)
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| Challenge: | Existing methods to generate semantic parsers that answer questions on databases require large amounts of annotated data. |
| Approach: | They propose a method to generate semantic parsers that answer questions on databases . they use automatic paraphrasing and template-based parsing to find alternative expressions . |
| Outcome: | The proposed method achieves 69.8% answer accuracy on natural questions, 16.4% higher than state-of-the-art models and 5.2% lower than the same model trained with human data. |
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing (P18-1)
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| Challenge: | Existing methods for semantic parsing are difficult to design and learn, especially in wideopen domains. |
| Approach: | They propose a neural semantic parsing approach which models semantic par- sing as an end-to-end semantic graph generation process. |
| Outcome: | The proposed model achieves state-of-the-art performance on Overnight dataset and gets competitive performance on Geo and Atis datasets. |
Unified Semantic Parsing with Weak Supervision (P19-1)
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Priyanka Agrawal, Ayushi Dalmia, Parag Jain, Abhishek Bansal, Ashish Mittal, Karthik Sankaranarayanan
| Challenge: | Semantic parsing over multiple knowledge bases requires high-quality annotations of (utterance, program) pairs. |
| Approach: | They propose a framework to build a unified multi-domain enabled semantic parser with weak supervision. |
| Outcome: | The proposed model improves performance by 20% on the Overnight dataset. |