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:20240329T000232Z 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-23312@www.clsp.jhu.edu DTSTAMP:20240329T000232Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAdvanced neural language models have grown ever large r and more complex\, pushing forward the limits of language understanding and generation\, while diminishing interpretability. The black-box nature of deep neural networks blocks humans from understanding them\, as well as trusting and using them in real-world applications. This talk will introd uce interpretation techniques that bridge the gap between humans and model s for developing trustworthy natural language processing(NLP). I will firs t show how to explain black-box models and evaluate their explanations for understanding their prediction behavior. Then I will introduce how to imp rove the interpretability of neural language models by making their decisi on-making transparent and rationalized. Finally\, I will discuss how to di agnose and improve models (e.g.\, robustness) through the lens of explanat ions. I will conclude with future research directions that are centered ar ound model interpretability and committed to facilitating communications a nd interactions between intelligent machines\, system developers\, and end users for long-term trustworthy AI.\nBiography\nHanjie Chen is a Ph.D. ca ndidate in Computer Science at the University of Virginia\, advised by Pro f. Yangfeng Ji. Her research interests lie in Trustworthy AI\, Natural Lan guage Processing (NLP)\, andInterpretable Machine Learning. She develops i nterpretation techniques to explain neural language models and make their prediction behavior transparent and reliable. She is a recipient of the Ca rlos and Esther Farrar Fellowship and the Best Poster Award at the ACM CAP WIC 2021. Her work has been published at top-tier NLP/AI conferences (e.g. \, ACL\, AAAI\, EMNLP\, NAACL) and selected by the National Center for Wom en & Information Technology (NCWIT) Collegiate Award Finalist 2021. She (a s the primary instructor) co-designed and taught the course\, Interpretabl e Machine Learning\, and was awarded the UVA CS Outstanding Graduate Teach ing Award and University-wide Graduate Teaching Awards Nominee (top 5% of graduate instructors). More details can be found athttps://www.cs.virginia .edu/~hc9mx DTSTART;TZID=America/New_York:20230313T120000 DTEND;TZID=America/New_York:20230313T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Hanjie Chen (University of Virginia) “Bridging Humans and Machines: Techniques for Trustworthy NLP” URL:https://www.clsp.jhu.edu/events/hanjie-chen-university-of-virginia/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nAdvanced neural language models have grown ever large
r and more complex\, pushing forward the limits of language understanding
and generation\, while diminishing interpretability. The black-box nature
of deep neural networks blocks humans from understanding them\, as well as
trusting and using them in real-world applications. This talk will introd
uce interpretation techniques that bridge the gap between humans and model
s for developing trustworthy natural language processing
(NLP). I will first show how to explain black-box models and evalua
te their explanations for understanding their prediction behavior. Then I
will introduce how to improve the interpretability of neural language mode
ls by making their decision-making transparent and rationalized. Finally\,
I will discuss how to diagnose and improve models (e.g.\, robustness) thr
ough the lens of explanations. I will conclude with future research direct
ions that are centered around model interpretability and committed to faci
litating communications and interactions between intelligent machines\, sy
stem developers\, and end users for long-term trustworthy AI.
Hanjie Chen is a Ph.D. candidate in Compute r Science at the University of Virginia\, advised by Prof. Yangfeng Ji. He r research interests lie in Trustworthy AI\, Natural Language Processing ( NLP)\, and
\n X-TAGS;LANGUAGE=en-US:2023\,Chen\,February END:VEVENT END:VCALENDAR Interpretable Machine Learning. She dev elops interpretation techniques to explain neural language models and make their prediction behavior transparent and reliable. She is a recipient of the Carlos and Esther Farrar Fellowship and the Best Poster Award at the ACM CAPWIC 2021. Her work has been published at top-tier NLP/AI conference s (e.g.\, ACL\, AAAI\, EMNLP\, NAACL) and selected by the National Center for Women & Information Technology (NCWIT) Collegiate Award Finalist 2021. She (as the primary instructor) co-designed and taught the course\, Inter pretable Machine Learning\, and was awarded the UVA CS Outstanding Graduat e Teaching Award and University-wide Graduate Teaching Awards Nominee (top 5% of graduate instructors). More details can be found at https://www.cs.virginia.edu/~hc9mx