US 12,323,262 B2
Systems and methods for coreference resolution
Tuan Manh Lai, Urbana, IL (US); Trung Huu Bui, San Jose, CA (US); and Doo Soon Kim, San Jose, CA (US)
Assigned to ADOBE INC., San Jose, CA (US)
Filed by ADOBE INC., San Jose, CA (US)
Filed on Jun. 14, 2022, as Appl. No. 17/806,751.
Prior Publication US 2023/0403175 A1, Dec. 14, 2023
Int. Cl. H04L 12/18 (2006.01); G06F 40/284 (2020.01); G06N 3/04 (2023.01)
CPC H04L 12/1831 (2013.01) [G06F 40/284 (2020.01); G06N 3/04 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method for coreference resolution, comprising:
obtaining a transcript including a first span of text separated from a second span of text;
determining that the first span of text comprises a name of a speaker of the second span of text;
inserting a speaker tag into the obtained transcript based on the determination, wherein the speaker tag comprises an opening tag inserted before the first span of text of the obtained transcript and a closing tag inserted after the first span of text of the obtained transcript, and wherein the speaker tag indicates that the first span of text comprises the name of the speaker;
encoding a plurality of candidate spans from the transcript using an encoder network of a machine learning model to obtain a plurality of span vectors, wherein the speaker tag is included in at least one candidate span of the plurality of candidate spans;
extracting a plurality of entity mentions from the transcript based on the plurality of span vectors using a mention extractor network of the machine learning model, wherein each of the plurality of entity mentions corresponds to one of the plurality of candidate spans; and
generating coreference information for the transcript based on the plurality of entity mentions using a mention linker network of the machine learning model, wherein the coreference information indicates that a pair of candidate spans of the plurality of candidate spans corresponds to a pair of entity mentions that refer to a same entity.