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Aug 22, 2019 · Yang Liu, Mirella Lapata. Bidirectional Encoder Representations from Transformers (BERT) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper, we showcase how BERT can be usefully applied in text summarization and propose a general framework ...
- Yang Liu, Mirella Lapata
- 2019
Dr. Bert Liu is a pulmonologist in Pasadena, California and is affiliated with multiple hospitals in the area, including Kaiser Permanente Downey Medical Center and Huntington Health Medical...
- Pasadena, CA
- (626) 486-0181
Residency: Keck SOM, LAC/USC Medical Center [06/30/2016 - 06/30/2019] Fellowship: UC Irvine Medical Center [07/01/2019 - 06/30/2022] Bert Liu, MD has a medical specialty in Pulmonary Disease and is affiliated with Huntington Hospital. Schedule your appointment with Bert Liu, MD in the city of PASADENA.
- 10 CONGRESS ST STE 155 PASADENA, CA 91105
- (626) 486-0181
2 days ago · In this paper, we showcase how BERT can be usefully applied in text summarization and propose a general framework for both extractive and abstractive models. We introduce a novel document-level encoder based on BERT which is able to express the semantics of a document and obtain representations for its sentences.
Mar 25, 2016 · Bert Liu an internist in 10 Congress Street Ste 155 Pasadena, Ca 91105. Phone: (626) 486-0181 Taxonomy code 207RP1001X with license number A154347 (CA) and 8 years of experience. He graduated from Boston University School Of Medicine in 2016. Provider is enrolled in PECOS Medicare.
2 days ago · Address: Online. Editors: Dan Jurafsky , Joyce Chai , Natalie Schluter , Joel Tetreault. Venue: ACL. SIG: Publisher: Association for Computational Linguistics. Note: Pages: 6035–6044. Language: URL: https://aclanthology.org/2020.acl-main.537. DOI: 10.18653/v1/2020.acl-main.537. Bibkey: liu-etal-2020-fastbert. Cite (ACL):
Jan 1, 2021 · 2 Overview of BERT Architecture. Fundamentally, BERT is a stack of Transformer encoder layers (Vaswani et al., 2017) that consist of multiple self-attention “heads”. For every input token in a sequence, each head computes key, value, and query vectors, used to create a weighted representation.
