Viewpoint: Ai as author - bridging the gap between machine learning and literary theory

Authors: Van Heerden, I. and Bas, A.

Journal: Journal of Artificial Intelligence Research

Volume: 71

Pages: 175-189

ISSN: 1076-9757

DOI: 10.1613/JAIR.1.12593

Abstract:

Anticipating the rise in Artificial Intelligence's ability to produce original works of literature, this study suggests that literariness, or that which constitutes a text as literary, is understudied in relation to text generation. From a computational perspective, literature is particularly challenging because it typically employs figurative and ambiguous language. Literary expertise would be beneficial to understanding how meaning and emotion are conveyed in this art form but is often overlooked. We propose placing experts from two dissimilar disciplines { machine learning and literary studies { in conversation to improve the quality of AI writing. Concentrating on evaluation as a vital stage in the text generation process, the study demonstrates that benefit could be derived from literary theoretical perspectives. This knowledge would improve algorithm design and enable a deeper understanding of how AI learns and generates.

https://eprints.bournemouth.ac.uk/39899/

Source: Scopus

Viewpoint: AI as Author - Bridging the Gap Between Machine Learning and Literary Theory.

Authors: Heerden, I.V. and Bas, A.

Journal: J. Artif. Intell. Res.

Volume: 71

Pages: 175-189

DOI: 10.1613/jair.1.12593

https://eprints.bournemouth.ac.uk/39899/

Source: DBLP

Viewpoint: AI as author - bridging the gap between machine learning and literary theory

Authors: Van Heerden, I. and Bas, A.

Journal: Journal of Artificial Intelligence Research

Volume: 71

Pages: 175-189

ISSN: 1076-9757

Abstract:

Anticipating the rise in Artificial Intelligence's ability to produce original works of literature, this study suggests that literariness, or that which constitutes a text as literary, is understudied in relation to text generation. From a computational perspective, literature is particularly challenging because it typically employs figurative and ambiguous language. Literary expertise would be beneficial to understanding how meaning and emotion are conveyed in this art form but is often overlooked. We propose placing experts from two dissimilar disciplines { machine learning and literary studies { in conversation to improve the quality of AI writing. Concentrating on evaluation as a vital stage in the text generation process, the study demonstrates that benefit could be derived from literary theoretical perspectives. This knowledge would improve algorithm design and enable a deeper understanding of how AI learns and generates.

https://eprints.bournemouth.ac.uk/39899/

Source: BURO EPrints