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:20240328T101701Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nI will present our work on data augmentation using style transfer as a way to im prove domain adaptation in sequence labeling tasks. The target domain is s ocial media data\, and the task is named entity recognition (NER). The pre mise is that we can transform the labelled out of domain data into somethi ng that stylistically is more closely related to the target data. Then we can train a model on a combination of the generated data and the smaller a mount of in domain data to improve NER prediction 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 Com puter Science at the University of Houston (UH). She holds graduate degree s in Computer Science from the Instituto Nacional de Astrofísica\, Óptica y Electrónica\, in Puebla\, Mexico. Her research interests include informa tion extraction from social media data\, enabling technology for code-swit ched data\, stylistic modeling of text\, and more recently multimodal appr oaches for online content understanding. She is the director and founder o f the RiTUAL Lab at UH. She is the recipient of an NSF CAREER award for he r work on authorship attribution\, and recipient of the 2014 Emerging Lead er ABIE Award in Honor of Denice Denton. She is currently serving a second term as an elected board member of the North American Chapter of the Asso ciation of Computational Linguistics and was PC co-chair for NAACL 2019. S he 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. p> 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-TAGS;LANGUAGE=en-US:2022\,September\,Solorio END:VEVENT BEGIN:VEVENT UID:ai1ec-23316@www.clsp.jhu.edu DTSTAMP:20240328T101701Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nUnderstanding the implicat ions underlying a text is critical to assessing its impact\, in particular the social dynamics that may result from a reading of the text. This requ ires endowing artificial intelligence (AI) systems with pragmatic reasonin g\, for example to correctly conclude that the statement “Epidemics and ca ses of disease in the 21st century are “staged”” relates to unfounded cons piracy theories. In this talk\, I discuss how shortcomings in the ability of current AI systems to reason about pragmatics present challenges to equ itable detection of false or harmful language. I demonstrate how these sho rtcomings can be addressed by imposing human-interpretable structure on de ep learning architectures using insights from linguistics.
\n< p> In the first part of the talk\, I descri be how adversarial text generation algorithms can be used to improve robus tness of content moderation systems. I then introduce a pragmatic formalis m for reasoning about harmful implications conveyed by social media text. I show how this pragmatic approach can be combined with generative neural language models to uncover implications of news headlines. I also address the bottleneck to progress in text generation posed by gaps in evaluation of factuality. I conclude by showing how context-aware content moderation can be used to ensure safe interactions with conversational agents. \nBiography
\nSaadia Gabriel is a PhD candidate in the Paul G. Al len School of Computer Science & Engineering at the University of Washingt on\, advised by Prof. Yejin Choi and Prof. Franziska Roesner. Her research revolves around natural language processing and m achine learning\, with a particular focus on building systems for understa nding how social commonsense manifests in text (i.e. how do people typical ly behave in social scenarios)\, as well as mitigating spread of false or harmful text (e.g. Covid-19 misinformation). Her work has been covered by a wide range of media outlets like Forbes and TechCrunch. It has also rece ived a 2019 ACL best short paper nomination\, a 2019 IROS RoboCup best pap er nomination and won a best paper award at the 2020 WeCNLP summit. Prior to her PhD\, Saadia received a BA summa cum laude from Mount Hol yoke College in Computer Science and Mathematics.
\nDTSTART;TZID=America/New_York:20230227T120000 DTEND;TZID=America/New_York:20230227T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Saadia Gabriel (University of Washington) “Socially Responsible and Factual Reasoning for Equitable AI Systems” URL:https://www.clsp.jhu.edu/events/saadia-gabriel-university-of-washington -socially-responsible-and-factual-reasoning-for-equitable-ai-systems/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,February\,Gabriel END:VEVENT END:VCALENDAR