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:20240329T134257Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nWhile there is a vast amount of text written about ne arly any topic\, this is often difficult for someone unfamiliar with a spe cific field to understand. Automated text simplification aims to reduce th e complexity of a document\, making it more comprehensible to a broader au dience. Much of the research in this field has traditionally focused on si mplification sub-tasks\, such as lexical\, syntactic\, or sentence-level s implification. However\, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many po or transformations\; on the other hand\, recent neural models\, while prod ucing grammatical output\, often do not make all needed changes to the ori ginal text. In this thesis\, I discuss novel approaches for improving lexi cal and sentence-level simplification systems. Regarding sentence simplifi cation models\, after noting that encouraging diversity at inference time leads to significant improvements\, I take a closer look at the idea of di versity and perform an exhaustive comparison of diverse decoding technique s on other generation tasks. I also discuss the limitations in the framing of current simplification tasks\, which prevent these models from yet bei ng practically useful. Thus\, I also propose a retrieval-based reformulati on of the problem. Specifically\, starting with a document\, I identify co ncepts critical to understanding its content\, and then retrieve documents relevant for each concept\, re-ranking them based on the desired complexi ty level.\nBiography\nI’m a research scientist at the HLTCOE at Johns Hopk ins University. My primary research interests are in language generation\, diverse and constrained decoding\, and information retrieval. During my P hD I focused mainly on the task of text simplification\, and now am workin g on formulating structured prediction problems as end-to-end generation t asks. I received my PhD in July 2021 from the University of Pennsylvania w ith Chris Callison-Burch and Marianna Apidianaki. DTSTART;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-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nWhile there is a vast amount of text written about ne arly any topic\, this is often difficult for someone unfamiliar with a spe cific field to understand. Automated text simplification aims to reduce th e complexity of a document\, making it more comprehensible to a broader au dience. Much of the research in this field has traditionally focused on si mplification sub-tasks\, such as lexical\, syntactic\, or sentence-level s implification. However\, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many po or transformations\; on the other hand\, recent neural models\, while prod ucing grammatical output\, often do not make all needed changes to the ori ginal text. In this thesis\, I discuss novel approaches for improving lexi cal and sentence-level simplification systems. Regarding sentence simplifi cation models\, after noting that encouraging diversity at inference time leads to significant improvements\, I take a closer look at the idea of di versity and perform an exhaustive comparison of diverse decoding technique s on other generation tasks. I also discuss the limitations in the framing of current simplification tasks\, which prevent these models from yet bei ng practically useful. Thus\, I also propose a retrieval-based reformulati on of the problem. Specifically\, starting with a document\, I identify co ncepts critical to understanding its content\, and then retrieve documents relevant for each concept\, re-ranking them based on the desired complexi ty level.
\nBiography
\nI ’m a research scientist at the HLTCOE at Johns Hopkins University. My prim ary research interests are in language generation\, diverse and constraine d decoding\, and information retrieval. During my PhD I focused mainly on the task of text simplification\, and now am working on formulating struct ured prediction problems as end-to-end generation tasks. I received my PhD in July 2021 from the University of Pennsylvania with Chris Callison-Burc h and Marianna Apidianaki.
\n\n X-TAGS;LANGUAGE=en-US:2021\,Kriz\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-24507@www.clsp.jhu.edu DTSTAMP:20240329T134257Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nHistory repeats itself\, sometimes in a bad way. Prev enting natural or man-made disasters requires being aware of these pattern s and taking pre-emptive action to address and reduce them\, or ideally\, eliminate them. Emerging events\, such as the COVID pandemic and the Ukrai ne Crisis\, require a time-sensitive comprehensive understanding of the si tuation to allow for appropriate decision-making and effective action resp onse. Automated generation of situation reports can significantly reduce t he time\, effort\, and cost for domain experts when preparing their offici al human-curated reports. However\, AI research toward this goal has been very limited\, and no successful trials have yet been conducted to automat e such report generation and “what-if” disaster forecasting. Pre-existing natural language processing and information retrieval techniques are insuf ficient to identify\, locate\, and summarize important information\, and l ack detailed\, structured\, and strategic awareness. In this talk I will p resent SmartBook\, a novel framework that cannot be solved by large langua ge models alone\, to consume large volumes of multimodal multilingual news data and produce a structured situation report with multiple hypotheses ( claims) summarized and grounded with rich links to factual evidence throug h multimodal knowledge extraction\, claim detection\, fact checking\, misi nformation detection and factual error correction. Furthermore\, SmartBook can also serve as a novel news event simulator\, or an intelligent prophe tess. Given “What-if” conditions and dimensions elicited from a domain ex pert user concerning a disaster scenario\, SmartBook will induce schemas f rom historical events\, and automatically generate a complex event graph a long with a timeline of news articles that describe new simulated events a nd character-centric stories based on a new Λ-shaped attention mask that c an generate text with infinite length. By effectively simulating disaster scenarios in both event graph and natural language format\, we expect Smar tBook will greatly assist humanitarian workers and policymakers to exercis e reality checks\, and thus better prevent and respond to future disasters .\nBio\nHeng Ji is a professor at Computer Science Department\, and an aff iliated faculty member at Electrical and Computer Engineering Department a nd Coordinated Science Laboratory of University of Illinois Urbana-Champai gn. She is an Amazon Scholar. She is the Founding Director of Amazon-Illin ois Center on AI for Interactive Conversational Experiences (AICE). She re ceived her B.A. and M. A. in Computational Linguistics from Tsinghua Unive rsity\, and her M.S. and Ph.D. in Computer Science from New York Universit y. Her research interests focus on Natural Language Processing\, especiall y on Multimedia Multilingual Information Extraction\, Knowledge-enhanced L arge Language Models\, Knowledge-driven Generation and Conversational AI. She was selected as a Young Scientist to attend the 6th World Laureates As sociation Forum\, and selected to participate in DARPA AI Forward in 2023. She was selected as “Young Scientist” and a member of the Global Future C ouncil on the Future of Computing by the World Economic Forum in 2016 and 2017. The awards she received include Women Leaders of Conversational AI ( Class of 2023) by Project Voice\, “AI’s 10 to Watch” Award by IEEE Intelli gent Systems in 2013\, NSF CAREER award in 2009\, PACLIC2012 Best paper ru nner-up\, “Best of ICDM2013” paper award\, “Best of SDM2013” paper award\, ACL2018 Best Demo paper nomination\, ACL2020 Best Demo Paper Award\, NAAC L2021 Best Demo Paper Award\, Google Research Award in 2009 and 2014\, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-20 18. She was invited to testify to the U.S. House Cybersecurity\, Data Anal ytics\, & IT Committee as an AI expert in 2023. She was invited by the Sec retary of the U.S. Air Force and AFRL to join Air Force Data Analytics Exp ert Panel to inform the Air Force Strategy 2030\, and invited to speak at the Federal Information Integrity R&D Interagency Working Group (IIRD IWG) briefing in 2023. She is the lead of many multi-institution projects and tasks\, including the U.S. ARL projects on information fusion and knowledg e networks construction\, DARPA ECOLE MIRACLE team\, DARPA KAIROS RESIN te am and DARPA DEFT Tinker Bell team. She has coordinated the NIST TAC Knowl edge Base Population task 2010-2022. She was the associate editor for IEEE /ACM Transaction on Audio\, Speech\, and Language Processing\, and served as the Program Committee Co-Chair of many conferences including NAACL-HLT2 018 and AACL-IJCNLP2022. She is elected as the North American Chapter of t he Association for Computational Linguistics (NAACL) secretary 2020-2023. Her research has been widely supported by the U.S. government agencies (DA RPA\, NSF\, DoE\, ARL\, IARPA\, AFRL\, DHS) and industry (Apple\, Amazon\, Google\, Facebook\, Bosch\, IBM\, Disney). DTSTART;TZID=America/New_York:20240405T120000 DTEND;TZID=America/New_York:20240405T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, Maryland 21218 SEQUENCE:0 SUMMARY:Heng Ji (University of Illinois Urbana-Champaign) “SmartBook: an AI Prophetess for Disaster Reporting and Forecasting” URL:https://www.clsp.jhu.edu/events/heng-ji-university-of-illinois-urbana-c hampaign-smartbook-an-ai-prophetess-for-disaster-reporting-and-forecasting / X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nHistory repeats itself\, sometimes in a bad way. Prev enting natural or man-made disasters requires being aware of these pattern s and taking pre-emptive action to address and reduce them\, or ideally\, eliminate them. Emerging events\, such as the COVID pandemic and the Ukrai ne Crisis\, require a time-sensitive comprehensive understanding of the si tuation to allow for appropriate decision-making and effective action resp onse. Automated generation of situation reports can significantly reduce t he time\, effort\, and cost for domain experts when preparing their offici al human-curated reports. However\, AI research toward this goal has been very limited\, and no successful trials have yet been conducted to automat e such report generation and “what-if” disaster forecasting. Pre-existing natural language processing and information retrieval techniques are insuf ficient to identify\, locate\, and summarize important information\, and l ack detailed\, structured\, and strategic awareness. In this talk I will p resent SmartBook\, a novel framework that cannot be solved by large langua ge models alone\, to consume large volumes of multimodal multilingual news data and produce a structured situation report with multiple hypotheses ( claims) summarized and grounded with rich links to factual evidence throug h multimodal knowledge extraction\, claim detection\, fact checking\, misi nformation detection and factual error correction. Furthermore\, SmartBook can also serve as a novel news event simulator\, or an intelligent prophe tess. Given “What-if” conditions and dimensions elicited from a domain ex pert user concerning a disaster scenario\, SmartBook will induce schemas f rom historical events\, and automatically generate a complex event graph a long with a timeline of news articles that describe new simulated events a nd character-centric stories based on a new Λ-shaped attention mask that c an generate text with infinite length. By effectively simulating disaster scenarios in both event graph and natural language format\, we expect Smar tBook will greatly assist humanitarian workers and policymakers to exercis e reality checks\, and thus better prevent and respond to future disasters .
\nBio
\nHeng Ji is a professor at Computer Science Department\, and an affiliated faculty member at Electrical and Co mputer Engineering Department and Coordinated Science Laboratory of Univer sity of Illinois Urbana-Champaign. She is an Amazon Scholar. She is the Fo unding Director of Amazon-Illinois Center on AI for Interactive Conversati onal Experiences (AICE). She received her B.A. and M. A. in Computational Linguistics from Tsinghua University\, and her M.S. and Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing\, especially on Multimedia Multilingual Information Ex traction\, Knowledge-enhanced Large Language Models\, Knowledge-driven Gen eration and Conversational AI. She was selected as a Young Scientist to at tend the 6th World Laureates Association Forum\, and selected to participa te in DARPA AI Forward in 2023. She was selected as “Young Scientist” and a member of the Global Future Council on the Future of Computing by the Wo rld Economic Forum in 2016 and 2017. The awards she received include Women Leaders of Conversational AI (Class of 2023) by Project Voice\, “AI’s 10 to Watch” Award by IEEE Intelligent Systems in 2013\, NSF CAREER award in 2009\, PACLIC2012 Best paper runner-up\, “Best of ICDM2013” paper award\, “Best of SDM2013” paper award\, ACL2018 Best Demo paper nomination\, ACL20 20 Best Demo Paper Award\, NAACL2021 Best Demo Paper Award\, Google Resear ch Award in 2009 and 2014\, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-2018. She was invited to testify to the U.S. House Cybersecurity\, Data Analytics\, & IT Committee as an AI expert in 2 023. She was invited by the Secretary of the U.S. Air Force and AFRL to jo in Air Force Data Analytics Expert Panel to inform the Air Force Strategy 2030\, and invited to speak at the Federal Information Integrity R&D Inter agency Working Group (IIRD IWG) briefing in 2023. She is the lead of many multi-institution projects and tasks\, including the U.S. ARL projects on information fusion and knowledge networks construction\, DARPA ECOLE MIRAC LE team\, DARPA KAIROS RESIN team and DARPA DEFT Tinker Bell team. She has coordinated the NIST TAC Knowledge Base Population task 2010-2022. She wa s the associate editor for IEEE/ACM Transaction on Audio\, Speech\, and La nguage Processing\, and served as the Program Committee Co-Chair of many c onferences including NAACL-HLT2018 and AACL-IJCNLP2022. She is elected as the North American Chapter of the Association for Computational Linguistic s (NAACL) secretary 2020-2023. Her research has been widely supported by t he U.S. government agencies (DARPA\, NSF\, DoE\, ARL\, IARPA\, AFRL\, DHS) and industry (Apple\, Amazon\, Google\, Facebook\, Bosch\, IBM\, Disney).
\n X-TAGS;LANGUAGE=en-US:2024\,April\,Ji END:VEVENT END:VCALENDAR