# Logic-Consistency Text Generation from Semantic Parses

arXiv: https://arxiv.org/abs/2108.00577 · HTML: https://mlsanity.com/paper/2108.00577/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 45 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (2020)
- [The E2E Dataset: New Challenges For End-to-End Generation](https://mlsanity.com/paper/1706.09254/) (2017)
- [Why We Need New Evaluation Metrics for NLG](https://mlsanity.com/paper/1707.06875/) (2017)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases](https://mlsanity.com/paper/1909.05378/) (2019)
- [Investigating Pretrained Language Models for Graph-to-Text Generation](https://mlsanity.com/paper/2007.08426/) (2020)
- [Learning a Neural Semantic Parser from User Feedback](https://mlsanity.com/paper/1704.08760/) (2017)
- [Logical Natural Language Generation from Open-Domain Tables](https://mlsanity.com/paper/2004.10404/) (2020)
- [Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation](https://mlsanity.com/paper/1910.08684/) (2019)
- [A Graph-to-Sequence Model for AMR-to-Text Generation](https://mlsanity.com/paper/1805.02473/) (2018)
- [Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints](https://mlsanity.com/paper/2005.00969/) (2020)
- [Logic2Text: High-Fidelity Natural Language Generation from Logical Forms](https://mlsanity.com/paper/2004.14579/) (2020)
- [Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity](https://mlsanity.com/paper/2004.06577/) (2020)
- [SQL-to-Text Generation with Graph-to-Sequence Model](https://mlsanity.com/paper/1809.05255/) (2018)
- [Revisiting Challenges in Data-to-Text Generation with Fact Grounding](https://mlsanity.com/paper/2001.03830/) (2020)
- [Enhancing AMR-to-Text Generation with Dual Graph Representations](https://mlsanity.com/paper/1909.00352/) (2019)
- [Structural Neural Encoders for AMR-to-text Generation](https://mlsanity.com/paper/1903.11410/) (2019)
- [Describing a Knowledge Base](https://mlsanity.com/paper/1809.01797/) (2018)
- [Effective Feature Representation for Clinical Text Concept Extraction](https://mlsanity.com/paper/1811.00070/) (2018)

## Built on by (most cited first, 23 of 23)

- [UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://mlsanity.com/paper/2201.05966/) (2022)
- [A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions](https://mlsanity.com/paper/2208.13629/) (2022)
- [SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Models](https://mlsanity.com/paper/2403.02951/) (2024)
- [StructLM: Towards Building Generalist Models for Structured Knowledge Grounding](https://mlsanity.com/paper/2402.16671/) (2024)
- [STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing](https://mlsanity.com/paper/2210.11888/) (2022)
- [PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation](https://mlsanity.com/paper/2205.12697/) (2022)
- [Multi-level Contrastive Learning for Script-based Character Understanding](https://mlsanity.com/paper/2310.13231/) (2023)
- [R2D2: Robust Data-to-Text with Replacement Detection](https://mlsanity.com/paper/2205.12467/) (2022)
- [LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control](https://mlsanity.com/paper/2302.02962/) (2023)
- [Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers](https://mlsanity.com/paper/2210.16613/) (2022)
- [XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations](https://mlsanity.com/paper/2306.04085/) (2023)
- [DistillCSE: Distilled Contrastive Learning for Sentence Embeddings](https://mlsanity.com/paper/2310.13499/) (2023)
- [Harnessing Knowledge and Reasoning for Human-Like Natural Language Generation: A Brief Review](https://mlsanity.com/paper/2212.03747/) (2022)
- [DECIDER: A Dual-System Rule-Controllable Decoding Framework for Language Generation](https://mlsanity.com/paper/2403.01954/) (2024)
- [Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples](https://mlsanity.com/paper/2210.08548/) (2022)
- [Learning from Multiple Sources for Data-to-Text and Text-to-Data](https://mlsanity.com/paper/2302.11269/) (2023)
- [A Statistical Analysis of LLMs' Self-Evaluation Using Proverbs](https://mlsanity.com/paper/2410.16640/) (2024)
- [REFORMER: A ChatGPT-Driven Data Synthesis Framework Elevating Text-to-SQL Models](https://mlsanity.com/paper/2504.04363/) (2025)
- [Contrastive Learning for Inference in Dialogue](https://mlsanity.com/paper/2310.12467/) (2023)
- [LLaSA: Large Language and Structured Data Assistant](https://mlsanity.com/paper/2411.14460/) (2024)
- [TrustDataFilter:Leveraging Trusted Knowledge Base Data for More Effective Filtering of Unknown Information](https://mlsanity.com/paper/2502.15714/) (2025)
- [VOLTAGE: A Versatile Contrastive Learning based OCR Methodology for ultra low-resource scripts through Auto Glyph Feature Extraction](https://mlsanity.com/paper/2510.10490/) (2025)
- [AI-Powered Annotation Pipelines for Stabilizing Large Language Models: A Human-AI Synergy Approach](https://mlsanity.com/paper/2512.13714/) (2025)

## Abstract (arXiv)

Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries. This is challenging due to two reasons: (1) the complex and intensive inner logic with the data scarcity constraint, (2) the lack of automatic evaluation metrics for logic consistency. To address these two challenges, this paper first proposes SNOWBALL, a framework for logic consistent text generation from semantic parses that employs an iterative training procedure by recursively augmenting the training set with quality control. Second, we propose a novel automatic metric, BLEC, for evaluating the logical consistency between the semantic parses and generated texts. The experimental results on two benchmark datasets, Logic2Text and Spider, demonstrate the SNOWBALL framework enhances the logic consistency on both BLEC and human evaluation. Furthermore, our statistical analysis reveals that BLEC is more logically consistent with human evaluation than general-purpose automatic metrics including BLEU, ROUGE and, BLEURT. Our data and code are available at https://github.com/Ciaranshu/relogic.