Papers with logical form
Semantic Parsing with Dual Learning (P19-1)
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| Challenge: | Existing approaches to parse natural language queries are limited by lack of labeled data and constrained decoding. |
| Approach: | They propose a semantic parsing framework with the dual learning algorithm that makes full use of data through a dual-learning game. |
| Outcome: | The proposed approach achieves state-of-the-art performance on ATIS dataset and gets competitive performance on overnight dataset. |
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information. |
| Approach: | They propose to use syntactic graph to represent three types of syntaktic information . they then employ a graph-to-sequence model to encode the syntastic graph and decode a logical form . |
| Outcome: | The proposed model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. |
Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing (D18-1)
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| Challenge: | Existing methods for training semantic parsers in new domains require expensive supervision and lack the ability to generalize to new domain. |
| Approach: | They propose a zero-shot approach to parsing utterances in unseen domains . they map an utterant to an abstract, domain independent, logical form and replace slots with KB constants based on lexical alignment scores and global inference . |
| Outcome: | The proposed model achieves 53.4% accuracy on 7 domains in the OVERNIGHT dataset, significantly better than other zero-shot baselines and performs as good as a parser trained on over 30% of the target domain examples. |
Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text (2023.findings-acl)
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| Challenge: | Large language models (LLMs) are robust and general, but their reasoning ability is not at a level to compete with the best models trained for specific natural language reasoning problems. |
| Approach: | They propose to use large language models as a few-shot semantic parser to convert natural language sentences into a logical form that serves as input for answer set programs. |
| Outcome: | The proposed model can handle multiple question-answering tasks without requiring retraining for each new task. |
Complex Question Decomposition for Semantic Parsing (P19-1)
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| Challenge: | Existing methods that ignore the decompositionality of complex questions are not suitable for complex question semantic parsing. |
| Approach: | They propose a hierarchical semantic parsing method which utilizes the decompositionality of complex questions for semantic paring. |
| Outcome: | The proposed method improves on a large scale complex question semantic parsing dataset. |
Answering Conversational Questions on Structured Data without Logical Forms (D19-1)
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| Challenge: | Existing approaches to answering sequential questions based on structured objects do not use a logical form as an intermediate representation. |
| Approach: | They propose a novel approach to answering sequential questions based on structured objects without using a logical form as an intermediate representation. |
| Outcome: | The proposed approach is competitively tested on the Sequential Question Answering (SQA) task. |
Case-based Reasoning for Natural Language Queries over Knowledge Bases (2021.emnlp-main)
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Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay Yoon Lee, Lizhen Tan, Lazaros Polymenakos, Andrew McCallum
| Challenge: | Using human-labeled examples, case-based reasoning can solve complex problems from scratch . case-Based reasoning is a paradigm that is used to solve complex problem . |
| Approach: | They propose a neuro-symbolic CBR approach for question answering over large knowledge bases. |
| Outcome: | The proposed approach outperforms the current state of the art on a CWQ dataset by 11% on accuracy. |
Which bird does not have wings: Negative-constrained KGQA with Schema-guided Semantic Matching and Self-directed Refinement (2026.findings-acl)
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| Challenge: | Existing KGQA benchmarks and methods are biased toward positive and calculation constraints. Negative constraints are neglected, although they frequently appear in real-world questions. |
| Approach: | They propose a task where each question contains at least one negative constraint and a corresponding dataset, NestKGQA. |
| Outcome: | The proposed framework outperforms baselines on both KGQA and NEST-KGQA benchmarks under few-shot settings. |