The Learning-Knowledge-Reasoning Paradigm for Natural Language Understanding and Question Answering

Mitra, Arindam

doi:10.4230/OASIcs.ICLP.2018.19

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Document Identifiers

DOI: 10.4230/OASIcs.ICLP.2018.19
URN: urn:nbn:de:0030-drops-98856

Author Details

Arindam Mitra

Arizona State University, Tempe, USA

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Arindam Mitra. The Learning-Knowledge-Reasoning Paradigm for Natural Language Understanding and Question Answering. In Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018). Open Access Series in Informatics (OASIcs), Volume 64, pp. 19:1-19:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/OASIcs.ICLP.2018.19

Abstract

Given a text, several questions can be asked. For some of these questions, the answer can be directly looked up from the text. However for several other questions, one might need to use additional knowledge and sophisticated reasoning to find the answer. Developing AI agents that can answer these kinds of questions and can also justify their answer is the focus of this research. Towards this goal, we use the language of Answer Set Programming as the knowledge representation and reasoning language for the agent. The question then arises, is how to obtain the additional knowledge? In this work we show that using existing Natural Language Processing parsers and a scalable Inductive Logic Programming algorithm it is possible to learn this additional knowledge (containing mostly commonsense knowledge) from question-answering datasets which then can be used for inference.

Subject Classification

ACM Subject Classification

Computing methodologies → Natural language processing
Computing methodologies → Knowledge representation and reasoning

Keywords

Natural Language Understanding
Question Answering
Knowledge Acquisition
Inductive Logic Programming
Knowledge Representation and Reasoning

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