公开(公告)号：US11775778B2

公开(公告)日：2023-10-03

申请号：US17090776

申请日：2020-11-05

Applicant: Microsoft Technology Licensing, LLC

Inventor： Zhuliu Li ,  Xiao Yan ,  Yiming Wang ,  Jaewon Yang

IPC: G06F40/00 ,  G06F40/58 ,  G06N3/08 ,  G06N3/04 ,  G06F40/295

CPC classification number: G06F40/58 ,  G06F40/295 ,  G06N3/04 ,  G06N3/08

Abstract: Embodiments of the disclosed technologies incorporate taxonomy information into a cross-lingual entity graph and input the taxonomy-informed cross-lingual entity graph into a graph neural network. The graph neural network computes semantic alignment scores for node pairs. The semantic alignment scores are used to determine whether a node pair represents a valid machine translation.

公开(公告)号：US20220207099A1

公开(公告)日：2022-06-30

申请号：US17136864

申请日：2020-12-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yiming Wang ,  Xiao Yan ,  Lin Zhu ,  Jaewon Yang ,  Yanen Li ,  Jacob Bollinger

IPC: G06F16/9535 ,  G06N3/04 ,  G06F16/9536 ,  G06F16/36 ,  G06N20/20

Abstract: Techniques for ranking skills using an ensemble machine learning approach are described. The outputs of two heterogenous, machine-learned models are combined to rank a set of skills that may be possessed by an end-user of an online service. Some subset of the highest-ranking skills is then presented to the end-user with a recommendation that the skills be added to the end-user's profile. The ensemble learning technique involves a concept referred to as “boosting”, in which a weaker performing model is enhanced (e.g., “boosted”) by a stronger performing model, when ranking the set of skills. Accordingly, by using a combination of models, better results are achieved than might be with either one of the individual models alone. Furthermore, the approach is scalable in ways that cannot be achieved with heuristic-based approaches.

公开(公告)号：US11461421B2

公开(公告)日：2022-10-04

申请号：US17136864

申请日：2020-12-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yiming Wang ,  Xiao Yan ,  Lin Zhu ,  Jaewon Yang ,  Yanen Li ,  Jacob Bollinger

IPC: G06F16/9535 ,  G06N3/04 ,  G06N20/20 ,  G06F16/36

Abstract: Techniques for ranking skills using an ensemble machine learning approach are described. The outputs of two heterogenous, machine-learned models are combined to rank a set of skills that may be possessed by an end-user of an online service. Some subset of the highest-ranking skills is then presented to the end-user with a recommendation that the skills be added to the end-user's profile. The ensemble learning technique involves a concept referred to as “boosting”, in which a weaker performing model is enhanced (e.g., “boosted”) by a stronger performing model, when ranking the set of skills. Accordingly, by using a combination of models, better results are achieved than might be with either one of the individual models alone. Furthermore, the approach is scalable in ways that cannot be achieved with heuristic-based approaches.