Center for Language and Speech Processing

Events

CLSP Student Seminar – Kelly Marchisio – “Efficient Multilingual NLP”

January 17, 2023
When: January 23, 2023 @ 12:00 pm – 1:15 pm
Where: Hackerman Hall B17, 3400 N. Charles Street, Baltimore, MD 21218

Abstract Kelly’s research spans three broad directions in multilingual NLP and representation learning: (1) diagnosing and fixing failure modes in translation technologies (2) data-efficient and low-resource NLP, and (3) compute-efficient NLP. This talk is an[…]

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Student Seminar

September 29, 2022
When: October 3, 2022 @ 12:00 pm – 1:15 pm
Where: Hackerman Hall B17, 3400 N. Charles Street, Baltimore, MD 21218

 

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Mark Hasegawa-Johnson (University of Illinois Urbana-Champaign) “Zipf’s Law Suggests a Three-Pronged Approach to Inclusive Speech Recognition”

September 15, 2022
When: December 9, 2022 @ 12:00 pm – 1:15 pm
Where: Hackerman Hall B17, 3400 N. Charles Street, Baltimore, MD 21218

Abstract Zipf’s law is commonly glossed by the aphorism “infrequent words are frequent,” but in practice, it has often meant that there are three types of words: frequent, infrequent, and out-of-vocabulary (OOV). Speech recognition solved[…]

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Minje Kim (Indiana University) “Personalized Speech Enhancement: Data- and Resource-Efficient Machine Learning”

September 15, 2022
When: December 2, 2022 @ 12:00 pm – 1:15 pm
Where: Hackerman Hall B17, 3400 N. Charles Street, Baltimore, MD 21218

Abstract One of the keys to success in machine learning applications is to improve each user’s personal experience via personalized models. A personalized model can be a more resource-efficient solution than a general-purpose model, too,[…]

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Hui Guan (University of Massachusetts Amherst) “Towards Accurate and Efficient Edge Computing Via Multi-Task Learning”

September 15, 2022
When: November 11, 2022 @ 12:00 pm – 1:15 pm
Where: Hackerman Hall B17, 3400 N. Charles Street, Baltimore, MD 21218

Abstract AI-powered applications increasingly adopt Deep Neural Networks (DNNs) for solving many prediction tasks, leading to more than one DNNs running on resource-constrained devices. Supporting many models simultaneously on a device is challenging due to[…]

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Upcoming Seminars

Sep
29
Mon
12:00 pm NUTMEG: Separating Signal From N... @ Hackerman Hall B17
NUTMEG: Separating Signal From N... @ Hackerman Hall B17
Sep 29 @ 12:00 pm – 1:15 pm
Abstract: NLP models often rely on human-labeled data for training and evaluation. Many approaches crowdsource this data from a large number of annotators with varying skills, backgrounds, and motivations, resulting in conflicting annotations. These conflicts[...]
Oct
3
Fri
12:00 pm Propaganda is already influencin... @ Hackerman Hall B17
Propaganda is already influencin... @ Hackerman Hall B17
Oct 3 @ 12:00 pm – 1:15 pm
Abstract Millions of people around the world query (prompt) large language models for information. While several studies have compellingly documented the persuasive potential of these models, there is limited evidence of who or what influences[...]

Center for Language and Speech Processing