Company Filing History:
Years Active: 2011-2025
Title: Mei Yang: Innovator in Task Representation Learning and Translation Systems
Introduction
Mei Yang is a prominent inventor based in Seattle, WA, known for her contributions to the fields of task representation learning and translation systems. With a total of 2 patents, she has made significant strides in enhancing the efficiency and effectiveness of technology applications.
Latest Patents
One of Mei Yang's latest patents is titled "Intent-based task representation learning using weak supervision." This patent describes systems and methods aimed at generating a general task embedding that represents task information. The generated task embedding includes predicted task information, allowing for a more specified representation of the task. This innovation can be utilized across various models and applications, enhancing the understanding of task data through an encoder. The intent extractor is trained on multiple auxiliary tasks with weak supervision, providing semantic augmentation to under-specified task texts.
Another notable patent is "HMM alignment for combining translation systems." This invention involves a computing system designed to produce an optimized translation hypothesis from text input. The system comprises multiple translation machines, each generating its own translation hypothesis. An optimization machine aligns these hypotheses using a hidden Markov model, ensuring a more accurate translation output.
Career Highlights
Mei Yang is currently employed at Microsoft Technology Licensing, LLC, where she continues to innovate and contribute to the advancement of technology. Her work focuses on improving task representation and translation systems, making her a valuable asset to her organization.
Collaborations
Throughout her career, Mei has collaborated with notable colleagues, including Xiaodong He and Jianfeng Gao. These collaborations have further enriched her work and contributed to the success of her inventions.
Conclusion
Mei Yang's innovative work in task representation learning and translation systems showcases her expertise and dedication to advancing technology. Her patents reflect her commitment to enhancing the efficiency of various applications, making her a significant figure in her field.