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-22412@www.clsp.jhu.edu DTSTAMP:20240329T134006Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nDriven by the goal of erad icating language barriers on a global scale\, machine translation has soli dified itself as a key focus of artificial intelligence research today. Ho wever\, such efforts have coalesced around a small subset of languages\, l eaving behind the vast majority of mostly low-resource languages. What doe s it take to break the 200 language barrier while ensuring safe\, high-qua lity results\, all while keeping ethical considerations in mind? In this t alk\, I introduce No Language Left Behind\, an initiative to break languag e barriers for low-resource languages. In No Language Left Behind\, we too k on the low-resource language translation challenge by first contextualiz ing the need for translation support through exploratory interviews with n ative speakers. Then\, we created datasets and models aimed at narrowing t he performance gap between low and high-resource languages. We proposed mu ltiple architectural and training improvements to counteract overfitting w hile training on thousands of tasks. Critically\, we evaluated the perform ance of over 40\,000 different translation directions using a human-transl ated benchmark\, Flores-200\, and combined human evaluation with a novel t oxicity benchmark covering all languages in Flores-200 to assess translati on safety. Our model achieves an improvement of 44% BLEU relative to the p revious state-of-the-art\, laying important groundwork towards realizing a universal translation system in an open-source manner.
\nBi ography
\nAngela is a research scientis t at Meta AI Research in New York\, focusing on supporting efforts in spee ch and language research. Recent projects include No Language Left Behind (https://ai.facebook.com/r esearch/no-language-left-behind/) and Universal Speech Translation for Unwritten Languages (https://ai.faceb ook.com/blog/ai-translation-hokkien/). Before translation\, Angela pre viously focused on research in on-device models for NLP and computer visio n and text generation.
\nDTSTART;TZID=America/New_York:20221118T120000 DTEND;TZID=America/New_York:20221118T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Angela Fan (Meta AI Research) “No Language Left Behind: Scaling Hu man-Centered Machine Translation” URL:https://www.clsp.jhu.edu/events/angela-fan-facebook/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,Fan\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-24507@www.clsp.jhu.edu DTSTAMP:20240329T134006Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nHistory repeats itself\, s ometimes in a bad way. Preventing natural or man-made disasters requires b eing aware of these patterns and taking pre-emptive action to address and reduce them\, or ideally\, eliminate them. Emerging events\, such as the C OVID pandemic and the Ukraine Crisis\, require a time-sensitive comprehens ive understanding of the situation to allow for appropriate decision-makin g and effective action response. Automated generation of situation reports can significantly reduce the time\, effort\, and cost for domain experts when preparing their official human-curated reports. However\, AI research toward this goal has been very limited\, and no successful trials have ye t been conducted to automate such report generation and “what-if” disaster forecasting. Pre-existing natural language processing and information ret rieval techniques are insufficient to identify\, locate\, and summarize im portant information\, and lack detailed\, structured\, and strategic aware ness. In this talk I will present SmartBook\, a novel framework that canno t be solved by large language models alone\, to consume large volumes of m ultimodal multilingual news data and produce a structured situation report with multiple hypotheses (claims) summarized and grounded with rich links to factual evidence through multimodal knowledge extraction\, claim detec tion\, fact checking\, misinformation detection and factual error correcti on. Furthermore\, SmartBook can also serve as a novel news event simulator \, or an intelligent prophetess. Given “What-if” conditions and dimension s elicited from a domain expert user concerning a disaster scenario\, Smar tBook will induce schemas from historical events\, and automatically gener ate a complex event graph along with a timeline of news articles that desc ribe new simulated events and character-centric stories based on a new Λ-s haped attention mask that can generate text with infinite length. By effec tively simulating disaster scenarios in both event graph and natural langu age format\, we expect SmartBook will greatly assist humanitarian workers and policymakers to exercise 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 Computer Engineering Department and Coordinated S cience Laboratory of University of Illinois Urbana-Champaign. She is an Am azon Scholar. She is the Founding Director of Amazon-Illinois Center on AI for Interactive Conversational 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 Extraction\, Knowledge-enhanced Large Language Mo dels\, Knowledge-driven Generation and Conversational AI. She was selected as a Young Scientist to attend the 6th World Laureates Association Forum\ , and selected to participate in DARPA AI Forward in 2023. She was selecte d as “Young Scientist” and a member of the Global Future Council on the Fu ture of Computing by the World Economic Forum in 2016 and 2017. The awards she received include Women Leaders of Conversational AI (Class of 2023) b y 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 De mo paper nomination\, ACL2020 Best Demo Paper Award\, NAACL2021 Best Demo Paper Award\, Google Research Award in 2009 and 2014\, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-2018. She was invi ted to testify to the U.S. House Cybersecurity\, Data Analytics\, & IT Com mittee as an AI expert in 2023. She was invited by the Secretary of the U. S. Air Force and AFRL to join Air Force Data Analytics Expert Panel to inf orm the Air Force Strategy 2030\, and invited to speak at the Federal Info rmation Integrity R&D Interagency Working Group (IIRD IWG) briefing in 202 3. She is the lead of many multi-institution projects and tasks\, includin g the U.S. ARL projects on information fusion and knowledge networks const ruction\, DARPA ECOLE MIRACLE team\, DARPA KAIROS RESIN team and DARPA DEF T Tinker Bell team. She has coordinated the NIST TAC Knowledge Base Popula tion task 2010-2022. She was the associate editor for IEEE/ACM Transaction on Audio\, Speech\, and Language Processing\, and served as the Program C ommittee Co-Chair of many conferences including NAACL-HLT2018 and AACL-IJC NLP2022. She is elected as the North American Chapter of the Association f or Computational Linguistics (NAACL) secretary 2020-2023. Her research has been widely supported by the U.S. government agencies (DARPA\, NSF\, DoE\ , ARL\, IARPA\, AFRL\, DHS) and industry (Apple\, Amazon\, Google\, Facebo ok\, 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-TAGS;LANGUAGE=en-US:2024\,April\,Ji END:VEVENT END:VCALENDAR