# An ASP-based Approach to Answering Natural Language Questions for Texts

arXiv: https://arxiv.org/abs/2112.11241 · HTML: https://mlsanity.com/paper/2112.11241/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Constraint Answer Set Programming without Grounding](https://mlsanity.com/paper/1804.11162/) (2018)
- [A Physician Advisory System for Chronic Heart Failure Management Based on Knowledge Patterns](https://mlsanity.com/paper/1610.08115/) (2016)
- [Knowledge-driven Natural Language Understanding of English Text and its Applications](https://mlsanity.com/paper/2101.11707/) (2021)
- [Computing Stable Models of Normal Logic Programs Without Grounding](https://mlsanity.com/paper/1709.00501/) (2017)
- [What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text](https://mlsanity.com/paper/1804.05435/) (2018)
- [SQuARE: Semantics-based Question Answering and Reasoning Engine](https://mlsanity.com/paper/2009.10239/) (2020)
- [An Application of ASP Theories of Intentions to Understanding Restaurant Scenarios: Insights and Narrative Corpus](https://mlsanity.com/paper/1810.00445/) (2018)
- [Information Extraction Tool Text2ALM: From Narratives to Action Language System Descriptions](https://mlsanity.com/paper/1909.08235/) (2019)

## Built on by (most cited first, 3 of 3)

- [EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning](https://mlsanity.com/paper/2403.10692/) (2024)
- [Neuro-Symbolic Contrastive Learning for Cross-domain Inference](https://mlsanity.com/paper/2502.09213/) (2025)
- [Logic-Guided Data Extraction with Answer Set Programming and Large Language Models](https://mlsanity.com/paper/2607.19365/) (2026)

## Abstract (arXiv)

An approach based on answer set programming (ASP) is proposed in this paper for representing knowledge generated from natural language texts. Knowledge in a text is modeled using a Neo Davidsonian-like formalism, which is then represented as an answer set program. Relevant commonsense knowledge is additionally imported from resources such as WordNet and represented in ASP. The resulting knowledge-base can then be used to perform reasoning with the help of an ASP system. This approach can facilitate many natural language tasks such as automated question answering, text summarization, and automated question generation. ASP-based representation of techniques such as default reasoning, hierarchical knowledge organization, preferences over defaults, etc., are used to model commonsense reasoning methods required to accomplish these tasks. In this paper, we describe the CASPR system that we have developed to automate the task of answering natural language questions given English text. CASPR can be regarded as a system that answers questions by "understanding" the text and has been tested on the SQuAD data set, with promising results.