BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-22375@www.clsp.jhu.edu DTSTAMP:20240329T104110Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nI will present our work on data augmentation using st yle transfer as a way to improve domain adaptation in sequence labeling ta sks. The target domain is social media data\, and the task is named entity recognition (NER). The premise is that we can transform the labelled out of domain data into something that stylistically is more closely related t o the target data. Then we can train a model on a combination of the gener ated data and the smaller amount of in domain data to improve NER predicti on performance. I will show recent empirical results on these efforts.\nIf time allows\, I will also give an overview of other research projects I’m currently leading at RiTUAL (Research in Text Understanding and Analysis of Language) lab. The common thread among all these research problems is t he scarcity of labeled data.\nBiography\nThamar Solorio is a Professor of Computer Science at the University of Houston (UH). She holds graduate deg rees in Computer Science from the Instituto Nacional de Astrofísica\, Ópti ca y Electrónica\, in Puebla\, Mexico. Her research interests include info rmation extraction from social media data\, enabling technology for code-s witched data\, stylistic modeling of text\, and more recently multimodal a pproaches for online content understanding. She is the director and founde r of the RiTUAL Lab at UH. She is the recipient of an NSF CAREER award for her work on authorship attribution\, and recipient of the 2014 Emerging L eader ABIE Award in Honor of Denice Denton. She is currently serving a sec ond term as an elected board member of the North American Chapter of the A ssociation of Computational Linguistics and was PC co-chair for NAACL 2019 . She recently joined the team of Editors in Chief for the ACL Rolling Rev iew (ARR) system. Her research is currently funded by the NSF and by ADOBE . DTSTART;TZID=America/New_York:20220923T120000 DTEND;TZID=America/New_York:20220923T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Thamar Solorio (University of Houston) “Style Transfer for Data Aug mentation in Sequence Labeling Tasks” URL:https://www.clsp.jhu.edu/events/thamar-solorio-university-of-houston-st yle-transfer-for-data-augmentation-in-sequence-labeling-tasks/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nI will present our work on data a ugmentation using style transfer as a way to improve domain adaptation in sequence labeling tasks. The target domain is social media data\, and the task is named entity recognition (NER). The premise is that we can transfo rm the labelled out of domain data into something that stylistically is mo re closely related to the target data. Then we can train a model on a comb ination of the generated data and the smaller amount of in domain data to improve NER prediction performance. I will show recent empirical results o n these efforts.
\nIf time allows\, I will also give an overview of other research projects I’m currently leading at RiTUA L (Research in Text Understanding and Analysis of Language) lab. The commo n thread among all these research problems is the scarcity of labeled data .
\nBiography
\nThamar Solorio is a Professor of Computer Science at the Univer sity of Houston (UH). She holds graduate degrees in Computer Science from the Instituto Nacional de Astrofísica\, Óptica y Electrónica\, in Puebla\, Mexico. Her research interests include information extraction from social media data\, enabling technology for code-switched data\, stylistic model ing of text\, and more recently multimodal approaches for online content u nderstanding. She is the director and founder of the RiTUAL Lab at UH. She is the recipient of an NSF CAREER award for her work on authorship attrib ution\, and recipient of the 2014 Emerging Leader ABIE Award in Honor of D enice Denton. She is currently serving a second term as an elected board m ember of the North American Chapter of the Association of Computational Li nguistics and was PC co-chair for NAACL 2019. She recently joined the team of Editors in Chief for the ACL Rolling Review (ARR) system. Her research is currently funded by the NSF and by ADOBE.
\n X-TAGS;LANGUAGE=en-US:2022\,September\,Solorio END:VEVENT BEGIN:VEVENT UID:ai1ec-23586@www.clsp.jhu.edu DTSTAMP:20240329T104110Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230410T120000 DTEND;TZID=America/New_York:20230410T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Ruizhe Huang URL:https://www.clsp.jhu.edu/events/student-seminar-ruizhe-huang/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,April\,Huang END:VEVENT BEGIN:VEVENT UID:ai1ec-23892@www.clsp.jhu.edu DTSTAMP:20240329T104110Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThe growing power in computing and AI promises a near -term future of human-machine teamwork. In this talk\, I will present my r esearch group’s efforts in understanding the complex dynamics of human-mac hine interaction and designing intelligent machines aimed to assist and co llaborate with people. I will focus on 1) tools for onboarding machine tea mmates and authoring machine assistance\, 2) methods for detecting\, and b roadly managing\, errors in collaboration\, and 3) building blocks of know ledge needed to enable ad hoc human-machine teamwork. I will also highligh t our recent work on designing assistive\, collaborative machines to suppo rt older adults aging in place.\nBiography\nChien-Ming Huang is the John C . Malone Assistant Professor in the Department of Computer Science at the Johns Hopkins University. His research focuses on designing interactive AI aimed to assist and collaborate with people. He publishes in top-tier ven ues in HRI\, HCI\, and robotics including Science Robotics\, HRI\, CHI\, a nd CSCW. His research has received media coverage from MIT Technology Revi ew\, Tech Insider\, and Science Nation. Huang completed his postdoctoral t raining at Yale University and received his Ph.D. in Computer Science at t he University of Wisconsin–Madison. He is a recipient of the NSF CAREER aw ard. https://www.cs.jhu.edu/~cmhuang/ DTSTART;TZID=America/New_York:20230915T120000 DTEND;TZID=America/New_York:20230915T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Chien-Ming Huang (Johns Hopkins University) “Becoming Teammates: De signing Assistive\, Collaborative Machines” URL:https://www.clsp.jhu.edu/events/chien-ming-huang-johns-hopkins-universi ty/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe growing power in computing and AI promises a near -term future of human-machine teamwork. In this talk\, I will present my r esearch group’s efforts in understanding the complex dynamics of human-mac hine interaction and designing intelligent machines aimed to assist and co llaborate with people. I will focus on 1) tools for onboarding machine tea mmates and authoring machine assistance\, 2) methods for detecting\, and b roadly managing\, errors in collaboration\, and 3) building blocks of know ledge needed to enable ad hoc human-machine teamwork. I will also highligh t our recent work on designing assistive\, collaborative machines to suppo rt older adults aging in place.
\nBiography
\nChien-Ming Huang is the John C. Malone Assistant Professor in the Departm ent of Computer Science at the Johns Hopkins University. His research focu ses on designing interactive AI aimed to assist and collaborate with peopl e. He publishes in top-tier venues in HRI\, HCI\, and robotics including S cience Robotics\, HRI\, CHI\, and CSCW. His research has received media co verage from MIT Technology Review\, Tech Insider\, and Science Nation. Hua ng completed his postdoctoral training at Yale University and received his Ph.D. in Computer Science at the University of Wisconsin–Madison. He is a recipient of the NSF CAREER award. https://www .cs.jhu.edu/~cmhuang/
\n X-TAGS;LANGUAGE=en-US:2023\,Huang\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-24479@www.clsp.jhu.edu DTSTAMP:20240329T104110Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nThe speech field is evolving to solve more challengin g scenarios\, such as multi-channel recordings with multiple simultaneous talkers. Given the many types of microphone setups out there\, we present the UniX-Encoder. It’s a universal encoder designed for multiple tasks\, a nd worked with any microphone array\, in both solo and multi-talker enviro nments. Our research enhances previous multichannel speech processing effo rts in four key areas: 1) Adaptability: Contrasting traditional models con strained to certain microphone array configurations\, our encoder is unive rsally compatible. 2) MultiTask Capability: Beyond the single-task focus o f previous systems\, UniX-Encoder acts as a robust upstream model\, adeptl y extracting features for diverse tasks including ASR and speaker recognit ion. 3) Self-Supervised Training: The encoder is trained without requiring labeled multi-channel data. 4) End-to-End Integration: In contrast to mod els that first beamform then process single-channels\, our encoder offers an end-to-end solution\, bypassing explicit beamforming or separation. To validate its effectiveness\, we tested the UniXEncoder on a synthetic mult i-channel dataset from the LibriSpeech corpus. Across tasks like speech re cognition and speaker diarization\, our encoder consistently outperformed combinations like the WavLM model with the BeamformIt frontend. DTSTART;TZID=America/New_York:20240311T200500 DTEND;TZID=America/New_York:20240311T210500 SEQUENCE:0 SUMMARY:Zili Huang (JHU) “Unix-Encoder: A Universal X-Channel Speech Encode r for Ad-Hoc Microphone Array Speech Processing” URL:https://www.clsp.jhu.edu/events/zili-huang-jhu-unix-encoder-a-universal -x-channel-speech-encoder-for-ad-hoc-microphone-array-speech-processing/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe speech field is evolving to solve more challenging scenarios\, such as multi-channel recordings wi th multiple simultaneous talkers. Given the many types of microphone setup s out there\, we present the UniX-Encoder. It’s a universal encoder design ed for multiple tasks\, and worked with any microphone array\, in both sol o and multi-talker environments. Our research enhances previous multichann el speech processing efforts in four key areas: 1) Adaptability: Contrasti ng traditional models constrained to certain microphone array configuratio ns\, our encoder is universally compatible. 2) MultiTask Capability: Beyon d the single-task focus of previous systems\, UniX-Encoder acts as a robus t upstream model\, adeptly extracting features for diverse tasks including ASR and speaker recognition. 3) Self-Supervised Training: The encoder is trained without requiring labeled multi-channel data. 4) End-to-End Integr ation: In contrast to models that first beamform then process single-chann els\, our encoder offers an end-to-end solution\, bypassing explicit beamf orming or separation. To validate its effectiveness\, we tested the UniXEn coder on a synthetic multi-channel dataset from the LibriSpeech corpus. Ac ross tasks like speech recognition and speaker diarization\, our encoder c onsistently outperformed combinations like the WavLM model with the Beamfo rmIt frontend.
\n X-TAGS;LANGUAGE=en-US:2024\,Huang\,March END:VEVENT END:VCALENDAR