Natural Language Generation for Non-Expert Users

TL;DR

This paper presents a novel system for automatically generating natural language descriptions from logical data without relying on templates or large corpora, aimed at non-expert users.

Contribution

It introduces a template-free approach that leverages existing natural language sentences and grammatical frameworks for natural language generation in applications.

Findings

System successfully generates descriptions in two use cases.

Can be used to create abstract Wikipedia entries.

Does not require large training datasets.

Abstract

Motivated by the difficulty in presenting computational results, especially when the results are a collection of atoms in a logical language, to users, who are not proficient in computer programming and/or the logical representation of the results, we propose a system for automatic generation of natural language descriptions for applications targeting mainstream users. Differently from many earlier systems with the same aim, the proposed system does not employ templates for the generation task. It assumes that there exist some natural language sentences in the application domain and uses this repository for the natural language description. It does not require, however, a large corpus as it is often required in machine learning approaches. The systems consist of two main components. The first one aims at analyzing the sentences and constructs a Grammatical Framework (GF) for given sentences and is implemented using the Stanford parser and an answer set program. The second component is for sentence construction and relies on GF Library. The paper includes two use cases to demostrate the capability of the system. As the sentence construction is done via GF, the paper includes a use case evaluation showing that the proposed system could also be utilized in addressing a challenge to create an abstract Wikipedia, which is recently discussed in the BlueSky session of the 2018 International Semantic Web Conference.