# Exploring Neural Models for Parsing Natural Language into First-Order Logic

arXiv: https://arxiv.org/abs/2002.06544 · HTML: https://mlsanity.com/paper/2002.06544/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2020.

## Built on by (most cited first, 16 of 16)

- [FOLIO: Natural Language Reasoning with First-Order Logic](https://mlsanity.com/paper/2209.00840/) (2022)
- [Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation](https://mlsanity.com/paper/2305.15541/) (2023)
- [Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text](https://mlsanity.com/paper/2105.03659/) (2021)
- [Formal Specifications from Natural Language](https://mlsanity.com/paper/2206.01962/) (2022)
- [Autoformalizing Natural Language to First-Order Logic: A Case Study in Logical Fallacy Detection](https://mlsanity.com/paper/2405.02318/) (2024)
- [Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression](https://mlsanity.com/paper/2505.13527/) (2025)
- [Neural Unification for Logic Reasoning over Natural Language](https://mlsanity.com/paper/2109.08460/) (2021)
- [StepProof: Step-by-step verification of natural language mathematical proofs](https://mlsanity.com/paper/2506.10558/) (2025)
- [Hermes: Unlocking Security Analysis of Cellular Network Protocols by Synthesizing Finite State Machines from Natural Language Specifications](https://mlsanity.com/paper/2310.04381/) (2023)
- [Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding](https://mlsanity.com/paper/2112.11805/) (2021)
- [A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective](https://mlsanity.com/paper/2202.04847/) (2022)
- [HySTER: A Hybrid Spatio-Temporal Event Reasoner](https://mlsanity.com/paper/2101.06644/) (2021)
- [Understanding Enthymemes in Argument Maps: Bridging Argument Mining and Logic-based Argumentation](https://mlsanity.com/paper/2408.08648/) (2024)
- [Neuro-Symbolic Strong-AI Robots with Closed Knowledge Assumption: Learning and Deductions](https://mlsanity.com/paper/2604.09567/) (2026)
- [Fixing FOLIO and MALLS: Verified Annotations and an LLM-assisted Framework to Focus Human Relabeling](https://mlsanity.com/paper/2606.02837/) (2026)
- [Continual Reasoning: Non-Monotonic Reasoning in Neurosymbolic AI using Continual Learning](https://mlsanity.com/paper/2305.02171/) (2023)

## Abstract (arXiv)

Semantic parsing is the task of obtaining machine-interpretable representations from natural language text. We consider one such formal representation - First-Order Logic (FOL) and explore the capability of neural models in parsing English sentences to FOL. We model FOL parsing as a sequence to sequence mapping task where given a natural language sentence, it is encoded into an intermediate representation using an LSTM followed by a decoder which sequentially generates the predicates in the corresponding FOL formula. We improve the standard encoder-decoder model by introducing a variable alignment mechanism that enables it to align variables across predicates in the predicted FOL. We further show the effectiveness of predicting the category of FOL entity - Unary, Binary, Variables and Scoped Entities, at each decoder step as an auxiliary task on improving the consistency of generated FOL. We perform rigorous evaluations and extensive ablations. We also aim to release our code as well as large scale FOL dataset along with models to aid further research in logic-based parsing and inference in NLP.