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:20240329T132949Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nDriven by the goal of eradicating language barriers o n a global scale\, machine translation has solidified itself as a key focu s of artificial intelligence research today. However\, such efforts have c oalesced around a small subset of languages\, leaving behind the vast majo rity of mostly low-resource languages. What does it take to break the 200 language barrier while ensuring safe\, high-quality results\, all while ke eping ethical considerations in mind? In this talk\, I introduce No Langua ge Left Behind\, an initiative to break language barriers for low-resource languages. In No Language Left Behind\, we took on the low-resource langu age translation challenge by first contextualizing the need for translatio n support through exploratory interviews with native speakers. Then\, we c reated datasets and models aimed at narrowing the performance gap between low and high-resource languages. We proposed multiple architectural and tr aining improvements to counteract overfitting while training on thousands of tasks. Critically\, we evaluated the performance of over 40\,000 differ ent translation directions using a human-translated benchmark\, Flores-200 \, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety. Our model achiev es an improvement of 44% BLEU relative to the previous state-of-the-art\, laying important groundwork towards realizing a universal translation syst em in an open-source manner.\nBiography\nAngela is a research scientist at Meta AI Research in New York\, focusing on supporting efforts in speech a nd language research. Recent projects include No Language Left Behind (htt ps://ai.facebook.com/research/no-language-left-behind/) and Universal Spee ch Translation for Unwritten Languages (https://ai.facebook.com/blog/ai-tr anslation-hokkien/). Before translation\, Angela previously focused on res earch in on-device models for NLP and computer vision and text generation. DTSTART;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-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nDriven by the goal of eradicating language barriers o n a global scale\, machine translation has solidified itself as a key focu s of artificial intelligence research today. However\, such efforts have c oalesced around a small subset of languages\, leaving behind the vast majo rity of mostly low-resource languages. What does it take to break the 200 language barrier while ensuring safe\, high-quality results\, all while ke eping ethical considerations in mind? In this talk\, I introduce No Langua ge Left Behind\, an initiative to break language barriers for low-resource languages. In No Language Left Behind\, we took on the low-resource langu age translation challenge by first contextualizing the need for translatio n support through exploratory interviews with native speakers. Then\, we c reated datasets and models aimed at narrowing the performance gap between low and high-resource languages. We proposed multiple architectural and tr aining improvements to counteract overfitting while training on thousands of tasks. Critically\, we evaluated the performance of over 40\,000 differ ent translation directions using a human-translated benchmark\, Flores-200 \, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety. Our model achiev es an improvement of 44% BLEU relative to the previous state-of-the-art\, laying important groundwork towards realizing a universal translation syst em in an open-source manner.
\nBiography
\nAngela is a research scientist at Meta AI Research in Ne w York\, focusing on supporting efforts in speech and language research. R ecent projects include No Language Left Behind (https://ai.facebook.com/research/no-language-left-be hind/) and Universal Speech Translation for Unwritten Languages (https://ai.facebook.com/blog/ai-translation -hokkien/). Before translation\, Angela previously focused on research in on-device models for NLP and computer vision and text generation.
\n\n X-TAGS;LANGUAGE=en-US:2022\,Fan\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-24481@www.clsp.jhu.edu DTSTAMP:20240329T132949Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nNatural language provides an intuitive and powerful i nterface to access knowledge at scale. Modern language systems draw inform ation from two rich knowledge sources: (1) information stored in their par ameters during massive pretraining and (2) documents retrieved at inferenc e time. Yet\, we are far from building systems that can reliably provide i nformation from such knowledge sources. In this talk\, I will discuss path s for more robust systems. In the first part of the talk\, I will present a module for scaling retrieval-based knowledge augmentation. We learn a co mpressor that maps retrieved documents into textual summaries prior to in- context integration. This not only reduces the computational costs but als o filters irrelevant or incorrect information. In the second half of the t alk\, I will discuss the challenges of updating knowledge stored in model parameters and propose a method to prevent models from reciting outdated i nformation by identifying facts that are prone to rapid change. I will con clude my talk by proposing an interactive system that can elicit informati on from users when needed.\nBiography\nEunsol Choi is an assistant profess or in the Computer Science department at the University of Texas at Austin . Prior to UT\, she spent a year at Google AI as a visiting researcher. He r research area spans natural language processing and machine learning. Sh e is particularly interested in interpreting and reasoning about text in a dynamic real world context. She is a recipient of a Facebook research fel lowship\, Google faculty research award\, Sony faculty award\, and an outs tanding paper award at EMNLP. She received a Ph.D. in computer science and engineering from University of Washington and B.A in mathematics and comp uter science from Cornell University. DTSTART;TZID=America/New_York:20240315T120000 DTEND;TZID=America/New_York:20240315T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21209 SEQUENCE:0 SUMMARY:Eunsol Choi (University of Texas at Austin) “Knowledge-Rich Languag e Systems in a Dynamic World” URL:https://www.clsp.jhu.edu/events/eunsol-choi-university-of-texas-at-aust in-knowledge-rich-language-systems-in-a-dynamic-world/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nNatural language provides an intuitive and powerful i nterface to access knowledge at scale. Modern language systems draw inform ation from two rich knowledge sources: (1) information stored in their par ameters during massive pretraining and (2) documents retrieved at inferenc e time. Yet\, we are far from building systems that can reliably provide i nformation from such knowledge sources. In this talk\, I will discuss path s for more robust systems. In the first part of the talk\, I will present a module for scaling retrieval-based knowledge augmentation. We learn a co mpressor that maps retrieved documents into textual summaries prior to in- context integration. This not only reduces the computational costs but als o filters irrelevant or incorrect information. In the second half of the t alk\, I will discuss the challenges of updating knowledge stored in model parameters and propose a method to prevent models from reciting outdated i nformation by identifying facts that are prone to rapid change. I will con clude my talk by proposing an interactive system that can elicit informati on from users when needed.
\nBiography
\nEunsol Choi is an assistant professor in the Computer Scie nce department at the University of Texas at Austin. Prior to UT\, she spe nt a year at Google AI as a visiting researcher. Her research area spans n atural language processing and machine learning. She is particularly inter ested in interpreting and reasoning about text in a dynamic real world con text. She is a recipient of a Facebook research fellowship\, Google facult y research award\, Sony faculty award\, and an outstanding paper award at EMNLP. She received a Ph.D. in computer science and engineering from Unive rsity of Washington and B.A in mathematics and computer science from Corne ll University.
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