# Declarative Question Answering over Knowledge Bases containing Natural Language Text with Answer Set Programming

## TL;DR

This paper introduces a novel approach for question answering over large natural language texts by combining Answer Set Programming with external NLP modules, achieving significant performance improvements.

## Contribution

It presents a method that integrates logical reasoning with NLP modules in ASP to handle large text-based knowledge bases for question answering.

## Key findings

- Achieves up to 18% performance gain over standard MCQ solvers.
- Effectively handles larger texts where traditional logical reasoning falters.
- Demonstrates the viability of ASP with external NLP modules for knowledge-based QA.

## Abstract

While in recent years machine learning (ML) based approaches have been the popular approach in developing end-to-end question answering systems, such systems often struggle when additional knowledge is needed to correctly answer the questions. Proposed alternatives involve translating the question and the natural language text to a logical representation and then use logical reasoning. However, this alternative falters when the size of the text gets bigger. To address this we propose an approach that does logical reasoning over premises written in natural language text. The proposed method uses recent features of Answer Set Programming (ASP) to call external NLP modules (which may be based on ML) which perform simple textual entailment. To test our approach we develop a corpus based on the life cycle questions and showed that Our system achieves up to $18\%$ performance gain when compared to standard MCQ solvers.