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-21270@www.clsp.jhu.edu DTSTAMP:20240328T210350Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:
Abstract
\nSocial media allows resear chers to track societal and cultural changes over time based on language a nalysis tools. Many of these tools rely on statistical algorithms which ne ed to be tuned to specific types of language. Recent studies have question ed the robustness of longitudinal analyses based on statistical methods du e to issues of temporal bias and semantic shift. To what extent are change s in semantics over time affecting the reliability of longitudinal analyse s? We examine this question through a case study: understanding shifts in mental health during the course of the COVID-19 pandemic. We demonstrate t hat a recently-introduced method for measuring semantic shift may be used to proactively identify failure points of language-based models and improv e predictive generalization over time. Ultimately\, we find that these ana lyses are critical to producing accurate longitudinal studies of social me dia.
DTSTART;TZID=America/New_York:20220207T120000 DTEND;TZID=America/New_York:20220207T131500 LOCATION:In Person or Virtual Option @ https://wse.zoom.us/j/96735183473 @ 234 Ames Hall\, 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Keith Harrigian “The Problem of Semantic Shift in Longitudinal Monitoring of Social Media: A Case Study on Mental Health d uring the COVID-19 Pandemic” URL:https://www.clsp.jhu.edu/events/student-seminar-keith-harrigian-the-pro blem-of-semantic-shift-in-longitudinal-monitoring-of-social-media-a-case-s tudy-on-mental-health-during-the-covid-19-pandemic/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,February\,Harrigian END:VEVENT BEGIN:VEVENT UID:ai1ec-21621@www.clsp.jhu.edu DTSTAMP:20240328T210350Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nSystems that support expre ssive\, situated natural language interactions are essential for expanding access to complex computing systems\, such as robots and databases\, to n on-experts. Reasoning and learning in such natural language interactions i s a challenging open problem. For example\, resolving sentence meaning req uires reasoning not only about word meaning\, but also about the interacti on context\, including the history of the interaction and the situated env ironment. In addition\, the sequential dynamics that arise between user an d system in and across interactions make learning from static data\, i.e.\ , supervised data\, both challenging and ineffective. However\, these same interaction dynamics result in ample opportunities for learning from impl icit and explicit feedback that arises naturally in the interaction. This lays the foundation for systems that continually learn\, improve\, and ada pt their language use through interaction\, without additional annotation effort. In this talk\, I will focus on these challenges and opportunities. First\, I will describe our work on modeling dependencies between languag e meaning and interaction context when mapping natural language in interac tion to executable code. In the second part of the talk\, I will describe our work on language understanding and generation in collaborative interac tions\, focusing on continual learning from explicit and implicit user fee dback.
\nBiography
\nAlane Suhr is a PhD Cand idate in the Department of Computer Science at Cornell University\, advis ed by Yoav Artzi. Her research spans natural language processing\, machine learning\, and computer vision\, with a focus on building systems that pa rticipate and continually learn in situated natural language interactions with human users. Alane’s work has been recognized by paper awards at ACL and NAACL\, and has been supported by fellowships and grants\, including a n NSF Graduate Research Fellowship\, a Facebook PhD Fellowship\, and resea rch awards from AI2\, ParlAI\, and AWS. Alane has also co-organized multip le workshops and tutorials appearing at NeurIPS\, EMNLP\, NAACL\, and ACL. Previously\, Alane received a BS in Computer Science and Engineering as a n Eminence Fellow at the Ohio State University.
DTSTART;TZID=America/New_York:20220314T120000 DTEND;TZID=America/New_York:20220314T131500 LOCATION:Virtual Seminar SEQUENCE:0 SUMMARY:Alane Suhr (Cornell University) “Reasoning and Learning in Interact ive Natural Language Systems” URL:https://www.clsp.jhu.edu/events/alane-suhr-cornell-university-reasoning -and-learning-in-interactive-natural-language-systems/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,March\,Suhr END:VEVENT BEGIN:VEVENT UID:ai1ec-21616@www.clsp.jhu.edu DTSTAMP:20240328T210350Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract
\nSocial media allows resear chers to track societal and cultural changes over time based on language a nalysis tools. Many of these tools rely on statistical algorithms which ne ed to be tuned to specific types of language. Recent studies have shown th e absence of appropriate tuning\, specifically in the presence of semantic shift\, can hinder robustness of the underlying methods. However\, little is known about the practical effect this sensitivity may have on downstre am longitudinal analyses. We explore this gap in the literature through a timely case study: understanding shifts in depression during the course of the COVID-19 pandemic. We find that inclusion of only a small number of s emantically-unstable features can promote significant changes in longitudi nal estimates of our target outcome. At the same time\, we demonstrate tha t a recently-introduced method for measuring semantic shift may be used to proactively identify failure points of language-based models and\, in tur n\, improve predictive generalization.
DTSTART;TZID=America/New_York:20220318T120000 DTEND;TZID=America/New_York:20220318T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Keith Harrigian “The Problem of Semantic Shift in Longitudinal Monitoring of Social Media” URL:https://www.clsp.jhu.edu/events/student-seminar-keith-harrigian-the-pro blem-of-semantic-shift-in-longitudinal-monitoring-of-social-media/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,Harrigian\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-24457@www.clsp.jhu.edu DTSTAMP:20240328T210350Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract
\nAs artificial intelligence (AI) continues to rapidly expand into existing healthcare infrastructure – e.g.\, clinical decision support\, administrative tasks\, and public hea lth surveillance – it is perhaps more important than ever to reflect on th e broader purpose of such systems. While much focus has been on the potent ial for this technology to improve general health outcomes\, there also ex ists a significant\, but understated\, opportunity to use this technology to address health-related disparities. Accomplishing the latter depends no t only on our ability to effectively identify addressable areas of systemi c inequality and translate them into tasks that are machine learnable\, bu t also our ability to measure\, interpret\, and counteract barriers in tra ining data that may inhibit robustness to distribution shift upon deployme nt (i.e.\, new populations\, temporal dynamics). In this talk\, we will di scuss progress made along both of these dimensions. We will begin by provi ding background on the state of AI for promoting health equity. Then\, we will present results from a recent clinical phenotyping project and discus s their implication on prevailing views regarding language model robustnes s in clinical applications. Finally\, we will showcase ongoing efforts to proactively address systemic inequality in healthcare by identifying and c haracterizing stigmatizing language in medical records.
DTSTART;TZID=America/New_York:20240226T120000 DTEND;TZID=America/New_York:20240226T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Keith Harrigian (JHU) “Fighting Bias From Bias: Robust Natural Lang uage Processing Techniques to Promote Health Equity” URL:https://www.clsp.jhu.edu/events/keith-harrigian-jhu-fighting-bias-from- bias-robust-natural-language-processing-techniques-to-promote-health-equit y/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,February\,Harrigian END:VEVENT END:VCALENDAR