US 11,727,210 B2
Structured graph-to-text generation with two step fine-tuning
Qingyun Wang, Urbana, IL (US); Nazneen Rajani, Mountain View, CA (US); Semih Yavuz, Redwood City, CA (US); and Xi Lin, Palo Alto, CA (US)
Assigned to Salesforce.com, Inc., San Francisco, CA (US)
Filed by salesforce.com, inc., San Francisco, CA (US)
Filed on Jan. 29, 2021, as Appl. No. 17/162,040.
Claims priority of provisional application 63/065,965, filed on Aug. 14, 2020.
Prior Publication US 2022/0050964 A1, Feb. 17, 2022
Int. Cl. G06F 40/284 (2020.01); G06F 40/205 (2020.01); G06F 40/10 (2020.01)
CPC G06F 40/284 (2020.01) [G06F 40/10 (2020.01); G06F 40/205 (2020.01)] 17 Claims
OG exemplary drawing
 
1. A system comprising:
a non-transitory memory; and
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
receiving, at a data-to-text generation system that includes a generative language model, an input data that includes resource description framework (RDF) triples in an RDF graph;
generating, using the data-to-text generation system, embeddings from the RDF graph based on tokens of the input data, wherein the embeddings include a position aware embedding that identifies a position of an RDF triple of the RDF triples in the RDF graph; and
generating, using the data-to-text generation system, a textual description of the input data based on the embeddings and the RDF graph,
wherein the position aware embedding includes a position embedding that identifies a position of a token indicating whether a word in the RDF triple of the RDF triples is a subject, a relation, or an object.