US 12,443,796 B2
Out of domain sentence detection
Haode Qi, Cambridge, MA (US); Cheng Qian, Toronto (CA); Ladislav Kunc, Cambridge, MA (US); Saloni Potdar, Arlington, MA (US); and Eric Donald Wayne, Raleigh, NC (US)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Jul. 28, 2022, as Appl. No. 17/815,630.
Prior Publication US 2024/0037331 A1, Feb. 1, 2024
Int. Cl. G06F 40/279 (2020.01); G06F 16/3329 (2025.01); G06F 40/30 (2020.01)
CPC G06F 40/279 (2020.01) [G06F 16/3329 (2019.01); G06F 40/30 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
training, by at least one processor of a device, an autoencoder to detect user inputs that are out of a domain in which a dialogue system has been trained, wherein the training comprises:
receiving, for the domain of the dialogue system, training user input representations, wherein the training user input representations comprise respective weights based on respective importance of the training user input representations to the training, and wherein the training user input representations comprise in-domain training user input representations and out-of-domain training user input representations;
encoding the in-domain training user input representations into encoded in-domain training user input representations and the out-of-domain training user input representations into encoded out-of-domain training user input representations in a latent space;
decoding, from the latent space, the encoded in-domain training user input representations into decoded in-domain training user input representations and the encoded out-of-domain training user input representations into decoded out-of-domain training user input representations;
minimizing an in-domain reconstruction error between the in-domain training user input representations and the decoded in-domain training user input representations respectively corresponding to the in-domain training user input representations, wherein the in-domain reconstruction error is based on the respective weights of the in-domain training user input representations; and
maximizing an out-of-domain reconstruction error between the out-of-domain training user input representations and the decoded out-of-domain training user input representations respectively corresponding to the out-of-domain training user input representations, wherein the out-of-domain reconstruction error is based on the respective weights of the out-of-domain training user input representations;
receiving, by the at least one processor, using the dialogue system, a user input from a user;
determining, by the at least one processor, using the autoencoder, whether the user input is out of the domain; and
responsive to determining that the user input is out of the domain, directing the user to a different dialogue system trained for another domain that is different from the domain.