Papers with neural semantic parser
Does Character-level Information Always Improve DRS-based Semantic Parsing? (2023.starsem-1)
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| Challenge: | incorporating character-level information does not improve the performance in English and German, and is not sensitive to correct character order in Dutch. |
| Approach: | They propose to incorporate character-level representations into a neural semantic parser for Discourse Representation Structures and to test their performance using order of character sequences. |
| Outcome: | The proposed parser improves in English, German, Dutch, and Italian in four languages. |
Photon: A Robust Cross-Domain Text-to-SQL System (2020.acl-demos)
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Jichuan Zeng, Xi Victoria Lin, Steven C.H. Hoi, Richard Socher, Caiming Xiong, Michael Lyu, Irwin King
| Challenge: | Existing natural language interfaces to databases are ambiguous or untranslatable . we present a robust, modular cross-domain text-to-SQL system . |
| Approach: | They propose a system that flags natural language input to which a SQL mapping cannot be immediately determined. |
| Outcome: | The proposed system can flag natural language input to which a SQL mapping cannot be determined. |
Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding (2022.emnlp-industry)
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Jun Wang, Patrick Ng, Alexander Hanbo Li, Jiarong Jiang, Zhiguo Wang, Bing Xiang, Ramesh Nallapati, Sudipta Sengupta
| Challenge: | Recent research on Text-to-SQL semantic parsing relies on parser or heuristic based approach to understand natural language query. |
| Approach: | They propose a general-purpose, modular neural semantic parsing framework that is based on token-level fine-grained query understanding. |
| Outcome: | The proposed framework outperforms the state-of-the-art model by 2.7% on a WikiTableQuestions test set. |
Program Synthesis for Complex QA on Charts via Probabilistic Grammar Based Filtered Iterative Back-Translation (2023.findings-eacl)
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Shabbirhussain Bhaisaheb, Shubham Paliwal, Rajaswa Patil, Manasi Patwardhan, Lovekesh Vig, Gautam Shroff
| Challenge: | Current chart-based Question Answering approaches address structural, visual or simple data retrieval-type questions with fixed-vocabulary answers. |
| Approach: | They employ a neural semantic parser to transform NL questions into SQL programs . they use a probabilistic context-free grammar to generate NL queries from a schema . |
| Outcome: | The proposed approach achieves State-of-the-Art (SOTA) results on reasoning-based queries. |
Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing (2022.emnlp-main)
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| Challenge: | Existing studies show that the design of Meaning Representation (MR) greatly influences the final model performance of a neural semantic parser. |
| Approach: | They propose a data-aware metric called ISS to measure the final performance of MRs. |
| Outcome: | The proposed metric denoting incremental structural stability (ISS) of MRs can be used as an indicator for MR design to avoid the costly training-testing process. |
Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers (2021.emnlp-main)
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| Challenge: | Existing methods for continual learning for semantic parsing fail to account for special properties of structured outputs . retraining from scratch is not feasible due to the fast growing number of tasks . |
| Approach: | They propose a continual learning method that uses sequential learning to learn tasks without accessing full training data from previous tasks. |
| Outcome: | The proposed method achieves a 3-6 times speedup compared to re-training from scratch. |
Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations (2022.coling-1)
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| Challenge: | Existing semantic parsers are based on deep learning, but rule-based approaches offer advantages . a drawback of neural semantic parses is that their output lacks explainability . |
| Approach: | They propose a method that maps a syntactic dependency tree to a formal meaning representation using a series of graph transformations. |
| Outcome: | The proposed method outperforms neural parsers in English, German, Italian and Dutch. |
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation (2022.emnlp-main)
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| Challenge: | Existing approaches to neural semantic parsing are limited by the semantic gap between natural and formal languages. |
| Approach: | They propose a unified intermediate representation for graph query languages, named GraphQ IR, which has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure. |
| Outcome: | The proposed representation can convert user queries into graphQ IR, which can later be losslessly compiled into various downstream graph query languages. |
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%. |
SPARQLing Database Queries from Intermediate Question Decompositions (2021.emnlp-main)
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| Challenge: | Using annotated datasets is difficult as it requires query-language expertise. |
| Approach: | They propose a crowdsourcing pipeline to annotate natural language questions using intermediate question representations. |
| Outcome: | The proposed pipeline reduces the burden of annotating a large dataset with queries by using intermediate question representations. |
Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing (2020.lrec-1)
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| Challenge: | Clinical trials require that patients meet eligibility criteria to ensure safety and effectiveness of studies. |
| Approach: | They propose a dataset that includes the first-of-its-kind eligibility-criteria corpus and queries for criteria-to-sql . they propose 'neuro semantic parser' which can translate eligibility criteria to executable SQL queries . |
| Outcome: | The proposed parser outperforms existing state-of-the-art general-purpose models while highlighting the challenges presented by the new dataset. |