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-20987@www.clsp.jhu.edu DTSTAMP:20240329T072526Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nWhile there is a vast amou nt of text written about nearly any topic\, this is often difficult for so meone unfamiliar with a specific field to understand. Automated text simpl ification aims to reduce the complexity of a document\, making it more com prehensible to a broader audience. Much of the research in this field has traditionally focused on simplification sub-tasks\, such as lexical\, synt actic\, or sentence-level simplification. However\, current systems strugg le to consistently produce high-quality simplifications. Phrase-based mode ls tend to make too many poor transformations\; on the other hand\, recent neural models\, while producing grammatical output\, often do not make al l needed changes to the original text. In this thesis\, I discuss novel ap proaches for improving lexical and sentence-level simplification systems. Regarding sentence simplification models\, after noting that encouraging d iversity at inference time leads to significant improvements\, I take a cl oser look at the idea of diversity and perform an exhaustive comparison of diverse decoding techniques on other generation tasks. I also discuss the limitations in the framing of current simplification tasks\, which preven t these models from yet being practically useful. Thus\, I also propose a retrieval-based reformulation of the problem. Specifically\, starting with a document\, I identify concepts critical to understanding its content\, and then retrieve documents relevant for each concept\, re-ranking them ba sed on the desired complexity level.
\nBiography
\nI’m a research scientist at the HLTCOE at Johns Hopkins University. My primary research interests are in language generati on\, diverse and constrained decoding\, and information retrieval. During my PhD I focused mainly on the task of text simplification\, and now am wo rking on formulating structured prediction problems as end-to-end generati on tasks. I received my PhD in July 2021 from the University of Pennsylvan ia with Chris Callison-Burch and Marianna Apidianaki.
\nDTSTART;TZID=America/New_York:20211022T120000 DTEND;TZID=America/New_York:20211022T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Reno Kriz (HLTCOE – JHU) “Towards a Practically Useful Text Simplif ication System” URL:https://www.clsp.jhu.edu/events/reno-kriz-hltcoe-jhu-towards-a-practica lly-useful-text-simplification-system/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2021\,Kriz\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-21041@www.clsp.jhu.edu DTSTAMP:20240329T072526Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nNarration is a universal h uman practice that serves as a key site of education\, collective memory\, fostering social belief systems\, and furthering human creativity. Recent studies in economics (Shiller\, 2020)\, climate science (Bushell et al.\, 2017)\, political polarization (Kubin et al.\, 2021)\, and mental health (Adler et al.\, 2016) suggest an emerging interdisciplinary consensus that narrative is a central concept for understanding human behavior and belie fs. For close to half a century\, the field of narratology has developed a rich set of theoretical frameworks for understanding narrative. And yet t hese theories have largely gone untested on large\, heterogenous collectio ns of texts. Scholars continue to generate schemas by extrapolating from s mall numbers of manually observed documents. In this talk\, I will discuss how we can use machine learning to develop data-driven theories of narrat ion to better understand what Labov and Waletzky called “the simplest and most fundamental narrative structures.” How can machine learning help us a pproach what we might call a minimal theory of narrativity?
\nAndrew Piper is Professor and William Dawson Scholar in the Department of Languages\, Literatures\, and Cultures at McGill University. He is the director of _.t xtlab
\n\na laboratory for cultural analytics\, and editor of the /Journal of Cultural Analytics/\, an open-access journal dedicated to the computational study of culture. He is the author of numerous books and articles on the relatio nship of technology and reading\, including /Book Was There: Reading in El ectronic Times/(Chicago 2012)\, /Enumerations: Data and Literary Study/(Ch icago 2018)\, and most recently\, /Can We Be Wrong? The Problem of Textual Evidence in a Time of Data/(Cambridge 2020).
DTSTART;TZID=America/New_York:20211112T120000 DTEND;TZID=America/New_York:20211112T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Andrew Piper (McGill University) ” How can we use machine learning to understand narration?” URL:https://www.clsp.jhu.edu/events/andrew-piper-mcgill-university-how-can- we-use-machine-learning-to-understand-narration/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2021\,November\,Piper END:VEVENT END:VCALENDAR