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:20240328T135816Z 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-21277@www.clsp.jhu.edu DTSTAMP:20240328T135816Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nAs humans\, our understand
ing of language is grounded in a rich mental model about “how the world wo
rks” – that we learn through perception and interaction. We use this under
standing to reason beyond what we literally observe or read\, imagining ho
w situations might unfold in the world. Machines today struggle at this ki
nd of reasoning\, which limits how they can communicate with humans.
In my talk\, I will discuss th
ree lines of work to bridge this gap between machines and humans. I will f
irst discuss how we might measure grounded understanding. I will introduce
a suite of approaches for constructing benchmarks\, using machines in the
loop to filter out spurious biases. Next\, I will introduce PIGLeT: a mod
el that learns physical commonsense understanding by interacting with the
world through simulation\, using this knowledge to ground language. From a
n English-language description of an event\, PIGLeT can anticipate how the
world state might change – outperforming text-only models that are orders
of magnitude larger. Finally\, I will introduce MERLOT\, which learns abo
ut situations in the world by watching millions of YouTube videos with tra
nscribed speech. Through training objectives inspired by the developmental
psychology idea of multimodal reentry\, MERLOT learns to fuse language\,
vision\, and sound together into powerful representations. Together\, these directions suggest a pa
th forward for building machines that learn language rooted in the world.<
/p>\n
Biography
\nRowan Zellers is a final year P hD candidate at the University of Washington in Computer Science & Enginee ring\, advised by Yejin Choi and Ali Farhadi. His research focuses on enab ling machines to understand language\, vision\, sound\, and the world beyo nd these modalities. He has been recognized through an NSF Graduate Fellow ship and a NeurIPS 2021 outstanding paper award. His work has appeared in several media outlets\, including Wired\, the Washington Post\, and the Ne w York Times. In the past\, he graduated from Harvey Mudd College with a B .S. in Computer Science & Mathematics\, and has interned at the Allen Inst itute for AI.
DTSTART;TZID=America/New_York:20220214T120000 DTEND;TZID=America/New_York:20220214T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rowan Zellers (University of Washington) ” Grounding Language by Se eing\, Hearing\, and Interacting” URL:https://www.clsp.jhu.edu/events/rowan-zellers-university-of-washington- grounding-language-by-seeing-hearing-and-interacting/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,February\,Zellers END:VEVENT END:VCALENDAR