| Challenge: | Semantic parsing aims to map natural language utterances into structured meaning representations. |
| Approach: | They propose a modular platform that allows developers to build semantic parser from scratch. |
| Outcome: | The proposed platform achieves competitive performance on semantic parsing task and improves performance of a business search engine. |
Similar Papers
Neural Semantic Parsing (P18-5)
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| Challenge: | Semantic parsing is the study of translating natural language utterances into machine-executable programs. |
| Approach: | They will describe the various approaches researchers have taken to translate natural language into a formal language . they will also discuss why much recent work has chosen to use standard programming languages instead of more linguistically-motivated representations. |
| Outcome: | This paper will describe the various approaches researchers have taken to translate natural language into a formal language. |
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. |
Coarse-to-Fine Decoding for Neural Semantic Parsing (P18-1)
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| Challenge: | Experimental results show that semantic parsing is more efficient than using simple decoders. |
| Approach: | They propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. |
| Outcome: | The proposed architecture consistently improves performance on four datasets characteristic of different domains and meaning representations. |
Context Dependent Semantic Parsing: A Survey (2020.coling-main)
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| Challenge: | Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations. |
| Approach: | They propose to use contextual information to translate natural language utterances into machine-readable meaning representations. |
| Outcome: | The proposed methods do not utilize contextual information, which could boost the semantic parsing systems. |
Semantic Parsing for English as a Second Language (2020.acl-main)
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| Challenge: | Existing studies on domain adaptation in NLP focus on learning challenges at the syntax-semantics interface during second language acquisition. |
| Approach: | They propose to use English Resource Grammar and TLE to parse ESL data using a reranking model to evaluate the quality of the annotations. |
| Outcome: | The proposed model can obtain a very promising quality in comparison to human annotations. |
Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing (2020.acl-main)
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| Challenge: | Existing evaluation datasets such as Spider are used to support cross-database semantic parsing . XSP systems that map natural language utterances to SQL queries are evaluated on databases unseen during training. |
| Approach: | They propose a setup that uses eight well-studied datasets to evaluate cross-database semantic parsing systems. |
| Outcome: | The proposed system performs well on spider, but struggles to generalize to the repurposed set. |
Parsing All: Syntax and Semantics, Dependencies and Spans (2020.findings-emnlp)
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| Challenge: | Syntactic and semantic structures are key linguistic contextual clues, but few studies have explored how they can be used to improve syntactical parsing. |
| Approach: | They propose a syntactic and semantic parsing model which integrates syntaktic information in the encoder of neural network and benefits from two representation formalisms in a uniform way. |
| Outcome: | The proposed model achieves state-of-the-art or competitive results on both span and dependency representations and on Penn Treebank. |
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation (D18-2)
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| Challenge: | Existing neural semantic parsers only focus on a small subset of tasks, such as SQL queries, robotic commands, and even general-purpose programming languages like Java. |
| Approach: | They propose a transition-based neural semantic parser that maps natural language utterances into formal meaning representations (MRs) they use an abstract syntax description language to constrain the output space and model the information flow. |
| Outcome: | Experiments on four different semantic parsing and code generation tasks show that the proposed system is generalizable, extensible, and effective. |
Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database (2022.emnlp-main)
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| Challenge: | Existing approaches on semantic parsing suffer from exponential growth of logical form candidates and can hardly generalize to unseen data. |
| Approach: | They propose a unified semantic parser for question answering on KB and DB . they define the primitive as the essential element in their framework . |
| Outcome: | The proposed framework can predict logical forms by altering and composing top-ranked primitives with different operations. |
Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints (2025.acl-short)
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| Challenge: | Semantic Parsing improves performance of smaller models, but it is unclear whether it extends similarly to large language models. |
| Approach: | They propose a prompting approach that embeds semantic hints within the prompt to improve LLM performance. |
| Outcome: | The proposed approach improves LLMs’ performance across various tasks, highlighting the potential of integrating semantic information to improve LLM capabilities. |