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-21489@www.clsp.jhu.edu DTSTAMP:20240329T091318Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nSince it is increasingly harder to opt out from inter acting with AI technology\, people demand that AI is capable of maintainin g contracts such that it supports agency and oversight of people who are r equired to use it or who are affected by it. To help those people create a mental model about how to interact with AI systems\, I extend the underly ing models to self-explain—predict the label/answer and explain this predi ction. In this talk\, I will present how to generate (1) free-text explana tions given in plain English that immediately tell users the gist of the r easoning\, and (2) contrastive explanations that help users understand how they could change the text to get another label.\nBiography\nAna Marasovi ć is a postdoctoral researcher at the Allen Institute for AI (AI2) and the Paul G. Allen School of Computer Science & Engineering at University of W ashington. Her research interests broadly lie in the fields of natural lan guage processing\, explainable AI\, and vision-and-language learning. Her projects are motivated by a unified goal: improve interaction and control of the NLP systems to help people make these systems do what they want wit h the confidence that they’re getting exactly what they need. Prior to joi ning AI2\, Ana obtained her PhD from Heidelberg University.\nHow to pronou nce my name: the first name is Ana like in Spanish\, i.e.\, with a long “a ” like in “water”\; regarding the last name: “mara” as in actress mara wil son + “so” + “veetch”. DTSTART;TZID=America/New_York:20220228T120000 DTEND;TZID=America/New_York:20220228T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Ana Marasović (Allen Institute for AI & University of Washington) “ Self-Explaining for Intuitive Interaction with AI” URL:https://www.clsp.jhu.edu/events/ana-marasovic-allen-institute-for-ai-un iversity-of-washington-self-explaining-for-intuitive-interaction-with-ai/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nSince it is increasingly harder to opt out from inter acting with AI technology\, people demand that AI is capable of maintainin g contracts such that it supports agency and oversight of people who are r equired to use it or who are affected by it. To help those people create a mental model about how to interact with AI systems\, I extend the underly ing models to self-explain—predict the label/answer and explain this predi ction. In this talk\, I will present how to generate (1) free-text explana tions given in plain English that immediately tell users the gist of the r easoning\, and (2) contrastive explanations that help users understand how they could change the text to get another label.
\nBiograph y
\nAna Marasović is a postdoctoral researcher at the Allen Institute for AI (AI2) and the Paul G. Allen School of Computer Science & Engineering at University of Washington. Her research interests broadly l ie in the fields of natural language processing\, explainable AI\, and vis ion-and-language learning. Her projects are motivated by a unified goal: i mprove interaction and control of the NLP systems to help people make thes e systems do what they want with the confidence that they’re getting exact ly what they need. Prior to joining AI2\, Ana obtained her PhD from Heidel berg University.
\nHow to pronounce my name: the first name i s Ana like in Spanish\, i.e.\, with a long “a” like in “water”\; regarding the last name: “mara” as in actress mara wilson + “so” + “veetch”.
\n< /BODY> X-TAGS;LANGUAGE=en-US:2022\,February\,Marasovic END:VEVENT BEGIN:VEVENT UID:ai1ec-22375@www.clsp.jhu.edu DTSTAMP:20240329T091318Z 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 END:VCALENDAR