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:20240329T115627Z 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-22400@www.clsp.jhu.edu DTSTAMP:20240329T115627Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nModern learning architectures for natural language processing have been very suc cessful in incorporating a huge amount of texts into their parameters. How ever\, by and large\, such models store and use knowledge in distributed a nd decentralized ways. This proves unreliable and makes the models ill-sui ted for knowledge-intensive tasks that require reasoning over factual info rmation in linguistic expressions. In this talk\, I will give a few examp les of exploring alternative architectures to tackle those challenges. In particular\, we can improve the performance of such (language) models by r epresenting\, storing and accessing knowledge in a dedicated memory compon ent.
\nThis talk is based on several joint works with Yury Zemlyanskiy (Google Research)\, Michiel de Jong (USC and Google Research)\, William Cohen (Google Research and CMU) and our other collabo rators in Google Research.
\nBiography
\nFei is a research scientist at Google Research. Before that\, he was a Profess or of Computer Science at University of Southern California. His primary r esearch interests are machine learning and its application to various AI p roblems: speech and language processing\, computer vision\, robotics and r ecently weather forecast and climate modeling. He has a PhD (2007) from Computer and Information Science from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing\, China).
DTSTART;TZID=America/New_York:20221024T120000 DTEND;TZID=America/New_York:20221024T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Fei Sha (University of Southern California) “Extracting Information from Text into Memory for Knowledge-Intensive Tasks” URL:https://www.clsp.jhu.edu/events/fei-sha-university-of-southern-californ ia/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,October\,Sha END:VEVENT END:VCALENDAR