US 11,734,509 B2
Controllable style-based text transformation
Abhijit Mishra, Bangalore (IN); Parag Jain, Bangalore (IN); Amar P. Azad, Bangalore (IN); and Karthik Sankaranarayanan, Bangalore (IN)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Dec. 29, 2020, as Appl. No. 17/136,437.
Application 17/136,437 is a continuation of application No. 16/371,492, filed on Apr. 1, 2019, granted, now 10,977,439.
Prior Publication US 2021/0117618 A1, Apr. 22, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 40/00 (2020.01); G06F 40/253 (2020.01); G06N 3/088 (2023.01); G06F 40/151 (2020.01)
CPC G06F 40/253 (2020.01) [G06F 40/151 (2020.01); G06N 3/088 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
selecting at least one set of style specifications for transforming at least a portion of input text, the at least one set of style specifications comprising: one or more target writing style domains selected from a plurality of writing style domains; weights for at least a portion of the target writing style domains representing relative impact of the target writing style domains for transformation of at least a portion of the input text; and weights for at least a portion of a set of linguistic aspects for transformation of at least a portion of the input text; and
generating one or more style-transformed output texts based at least in part on the at least one set of style specifications utilizing at least one unsupervised neural network;
wherein the method is carried out by at least one processing device.
 
12. A computer program product, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by at least one computing device to cause the at least one computing device to perform steps of:
selecting at least one set of style specifications for transforming at least a portion of input text, the at least one set of style specifications comprising: one or more target writing style domains selected from a plurality of writing style domains; weights for at least a portion of the target writing style domains representing relative impact of the target writing style domains for transformation of at least a portion of the input text; and weights for at least a portion of a set of linguistic aspects for transformation of at least a portion of the input text; and
generating one or more style-transformed output texts based at least in part on the at least one set of style specifications utilizing at least one unsupervised neural network.