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:20240328T132025Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nSocial media allows researchers to track societal and cultural changes over time based on language analysis tools. Many of thes e tools rely on statistical algorithms which need to be tuned to specific types of language. Recent studies have questioned the robustness of longit udinal analyses based on statistical methods due to issues of temporal bia s and semantic shift. To what extent are changes in semantics over time af fecting the reliability of longitudinal analyses? We examine this question through a case study: understanding shifts in mental health during the co urse of the COVID-19 pandemic. We demonstrate that a recently-introduced m ethod for measuring semantic shift may be used to proactively identify fai lure points of language-based models and improve predictive generalization over time. Ultimately\, we find that these analyses are critical to produ cing accurate longitudinal studies of social media. 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-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nSocial media allows researchers to track societal and cultural changes over time based on language analysis tools. Many of thes e tools rely on statistical algorithms which need to be tuned to specific types of language. Recent studies have questioned the robustness of longit udinal analyses based on statistical methods due to issues of temporal bia s and semantic shift. To what extent are changes in semantics over time af fecting the reliability of longitudinal analyses? We examine this question through a case study: understanding shifts in mental health during the co urse of the COVID-19 pandemic. We demonstrate that a recently-introduced m ethod for measuring semantic shift may be used to proactively identify fai lure points of language-based models and improve predictive generalization over time. Ultimately\, we find that these analyses are critical to produ cing accurate longitudinal studies of social media.
\n X-TAGS;LANGUAGE=en-US:2022\,February\,Harrigian END:VEVENT BEGIN:VEVENT UID:ai1ec-24425@www.clsp.jhu.edu DTSTAMP:20240328T132025Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\n\nOver the past three decades\, the fields of automat ic speech recognition (ASR) and machine translation (MT) have witnessed re markable advancements\, leading to exciting research directions such as sp eech-to-text translation (ST). This talk will delve into the domain of con versational ST\, an essential facet of daily communication\, which present s unique challenges including spontaneous informal language\, the presence of disfluencies\, high context dependence and a scarcity of ST paired dat a.\n\nConversational speech is notably characterized by its reliance on sh ort segments\, requiring the integration of broader contexts to maintain c onsistency and improve the translation’s fluency and quality. Incorporati ng longer contexts has been shown to benefit machine translation\, but the inclusion of context in E2E-ST remains under-studied. Previous approaches have used simple concatenation of audio inputs for context\, leading to m emory bottlenecks\, especially in self-attention networks\, due to the enc oding of lengthy audio segments.\n\nFirst\, I will describe how to integra te the context into E2E-ST with minimum additional memory cost. Then\, I will discuss the challenges of incorporating context in an E2E-ST system w ith limited data during training and inference and propose solutions to ov ercome them. Afterward\, I will illustrate the impact of context size and the inclusion of speaker information on performance. Lastly\, I will demon strate the benefits of context in conversational settings focusing on asp ects like anaphora resolution and the identification of named entities. DTSTART;TZID=America/New_York:20240205T120000 DTEND;TZID=America/New_York:20240205T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Amir Hussein “Towards End-to-End Conversational Speech Translation” URL:https://www.clsp.jhu.edu/events/amir-hussein-towards-end-to-end-convers ational-speech-translation/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr
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Over the past three decades\, the fields of automatic speech recognition (ASR) and machine tra nslation (MT) have witnessed remarkable advancements\, leading to exciting research directions such as speech-to-text translation (ST). This talk wi ll delve into the domain of conversational ST\, an essential facet of dail y communication\, which presents unique challenges including spontaneous i nformal language\, the presence of disfluencies\, high context dependence and a scarcity of ST paired data.
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\nWe introduce STAR (Stream Transduction with Anchor Re presentations)\, a novel Transformer-based model designed for efficient se quence-to-sequence transduction over streams. STAR dynamically segments in put streams to create compressed anchor representations\, achieving nearly lossless compression (12x) in Automatic Speech Recognition (ASR) and outp erforming existing methods. Moreover\, STAR demonstrates superior segmenta tion and latency-quality trade-offs in simultaneous speech-to-text tasks\, optimizing latency\, memory footprint\, and quality.
\n X-TAGS;LANGUAGE=en-US:2024\,February\,Tan END:VEVENT BEGIN:VEVENT UID:ai1ec-24457@www.clsp.jhu.edu DTSTAMP:20240328T132025Z 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 health surveillance – it is pe rhaps more important than ever to reflect on the broader purpose of such s ystems. While much focus has been on the potential for this technology to improve general health outcomes\, there also exists a significant\, but un derstated\, opportunity to use this technology to address health-related d isparities. Accomplishing the latter depends not only on our ability to ef fectively identify addressable areas of systemic inequality and translate them into tasks that are machine learnable\, but also our ability to measu re\, interpret\, and counteract barriers in training data that may inhibit robustness to distribution shift upon deployment (i.e.\, new populations\ , temporal dynamics). In this talk\, we will discuss progress made along b oth of these dimensions. We will begin by providing background on the stat e of AI for promoting health equity. Then\, we will present results from a recent clinical phenotyping project and discuss their implication on prev ailing views regarding language model robustness in clinical applications. Finally\, we will showcase ongoing efforts to proactively address systemi c inequality in healthcare by identifying and characterizing 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-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nAs artificial intelligence (AI) continues to rapidly expand into existing healthcare infrastructure – e.g.\, clinical decision support\, administrative tasks\, and public health surveillance – it is pe rhaps more important than ever to reflect on the broader purpose of such s ystems. While much focus has been on the potential for this technology to improve general health outcomes\, there also exists a significant\, but un derstated\, opportunity to use this technology to address health-related d isparities. Accomplishing the latter depends not only on our ability to ef fectively identify addressable areas of systemic inequality and translate them into tasks that are machine learnable\, but also our ability to measu re\, interpret\, and counteract barriers in training data that may inhibit robustness to distribution shift upon deployment (i.e.\, new populations\ , temporal dynamics). In this talk\, we will discuss progress made along b oth of these dimensions. We will begin by providing background on the stat e of AI for promoting health equity. Then\, we will present results from a recent clinical phenotyping project and discuss their implication on prev ailing views regarding language model robustness in clinical applications. Finally\, we will showcase ongoing efforts to proactively address systemi c inequality in healthcare by identifying and characterizing stigmatizing language in medical records.
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