US 12,265,792 B2
Generating commonsense context for text using knowledge graphs
Rachit Bansal, New Delhi (IN); Milan Aggarwal, Delhi (IN); Sumit Bhatia, New Delhi (IN); Jivat Neet Kaur, Delhi (IN); and Balaji Krishnamurthy, Nodia (IN)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by ADOBE INC., San Jose, CA (US)
Filed on Nov. 15, 2021, as Appl. No. 17/526,824.
Prior Publication US 2023/0153534 A1, May 18, 2023
Int. Cl. G06F 40/295 (2020.01); G06F 16/332 (2019.01); G06F 16/3329 (2025.01); G06N 20/00 (2019.01)
CPC G06F 40/295 (2020.01) [G06F 16/3329 (2019.01); G06N 20/00 (2019.01)] 19 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
generating a commonsense path derived using a commonsense knowledge graph, the commonsense path including a sequence comprising entities and relations;
generating a training path-sentence pair including the commonsense path from the commonsense knowledge graph and a natural language sentence identified as contextually-related to the commonsense path based on at least one entity or relation associated with the commonsense path, wherein the natural language sentence is identified as contextually-related to the commonsense path using a query including an entity and a relation from the commonsense path generated via a query template to query a set of natural language sentences and obtain a subset of natural language sentences determined to be relevant to the query and using a model that derives semantically meaningful sentence embeddings to identify the natural language sentence, from the subset of natural language sentences, as within a threshold of similarity to the commonsense path;
using the training path-sentence pair to train a commonsense contextualizing machine learning model comprising a pre-trained language model;
using the commonsense contextualizing machine learning model to generate a commonsense inference path for an input text provided to the commonsense contextualizing machine learning model, the commonsense inference path including a sequence comprising entities and relations that provide commonsense context to the input text, the commonsense inference path generated by concatenating a set of path tokens, wherein each path token represents an entity or a relation at a corresponding time-step associated with the commonsense contextualizing machine learning model; and
providing, to a natural language processing system, the input text and the commonsense inference path to augment the input text for use in performing a natural language processing task using at least the commonsense inference path.