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-20730@www.clsp.jhu.edu DTSTAMP:20240329T051030Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nRaytheon BBN participated in the IARPA MATERIAL program\, whose objective is to enable rapid develop ment of language-independent methods for cross-lingual information retriev al (CLIR). The challenging CLIR task of retrieving documents written (or s poken) in one language so that they satisfy an information need expressed in a different language is exacerbated by unique challenges posed by the M ATERIAL program: limited training data for automatic speech recognition an d machine translation\, scant lexical resources\, non-standardized orthogr aphy\, etc. Furthermore\, the format of the queries and the “Query-Weighte d Value” performance measure are non-standard and not previously studied i n the IR community. In this talk\, we will describe the Raytheon BBN CLIR system\, which was successful at addressing the above challenges and uniqu e characteristics of the program.
\nBiography
\nDamianos Karakos has been at Raytheon BBN f or the past nine years\, where he is currently a Senior Principal Engineer \, Research. Before that\, he was research faculty at Johns Hopkins Univer sity. He has worked on several Government projects (e.g.\, DARPA GALE\, DA RPA RATS\, IARPA BABEL\, IARPA MATERIAL\, IARPA BETTER) and on a variety o f HLT-related topics (e.g.\, speech recognition\, speech activity detectio n\, keyword search\, information retrieval). He has published more than 60 peer-reviewed papers. His research interests lie at the intersection of h uman language technology and machine learning\, with an emphasis on statis tical methods. He obtained a PhD in Electrical Engineering from the Univer sity of Maryland\, College Park\, in 2002.
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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-22417@www.clsp.jhu.edu DTSTAMP:20240329T051030Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nOne of the keys to success in machine learning applications is to improve each user’s personal exper ience via personalized models. A personalized model can be a more resource -efficient solution than a general-purpose model\, too\, because it focuse s on a particular sub-problem\, for which a smaller model architecture can be good enough. However\, training a personalized model requires data fro m the particular test-time user\, which are not always available due to th eir private nature and technical challenges. Furthermore\, such data tend to be unlabeled as they can be collected only during the test time\, once after the system is deployed to user devices. One could rely on the genera lization power of a generic model\, but such a model can be too computatio nally/spatially complex for real-time processing in a resource-constrained device. In this talk\, I will present som e techniques to circumvent the lack of labeled personal data in the contex t of speech enhancement. Our machine learning models will require zero or few data samples from the test-time users\, while they can still achieve t he personalization goal. To this end\, we will investigate modularized spe ech enhancement models as well as the potential of self-supervised learnin g for personalized speech enhancement. Because our research achieves the p ersonalization goal in a data- and resource-efficient way\, it is a step t owards a more available and affordable AI for society.
\nBio graphy
\nMinje Kim is an associate professor in the Dept. of Intellig ent Systems Engineering at Indiana University\, where he leads his researc h group\, Signals and AI Group in Engineering (SAIGE). He is also an Amazo n Visiting Academic\, consulting for Amazon Lab126. At IU\, he is affiliat ed with various programs and labs such as Data Science\, Cognitive Science \, Dept. of Statistics\, and Center for Machine Learning. He earned his Ph .D. in the Dept. of Computer Science at the University of Illinois at Urba na-Champaign. Before joining UIUC\, He worked as a researcher at ETRI\, a national lab in Korea\, from 2006 to 2011. Before then\, he received his M aster’s and Bachelor’s degrees in the Dept. of Computer Science and Engine ering at POSTECH (Summa Cum Laude) and in the Division of Information and Computer Engineering at Ajou University (w ith honor) in 2006 and 2004\, respectively. He is a recipient of various a wards including NSF Career Award (2021)\, IU Trustees Teaching Award (2021 )\, IEEE SPS Best Paper Award (2020)\, and Google and Starkey’s grants for outstanding student papers in ICASSP 2013 and 2014\, respectively. He is an IEEE Senior Member and also a member of the IEEE Audio and Acoustic Sig nal Processing Technical Committee (2018-2023). He is serving as an Associ ate Editor for EURASIP Journal of Audio\, Speech\, and Music Processing\, and as a Consulting Associate Editor for IEEE Open Journal of Signal Proce ssing. He is also a reviewer\, program committee member\, or area chair fo r the major machine learning and signal processing. He filed more than 50 patent applications as an inventor.
DTSTART;TZID=America/New_York:20221202T120000 DTEND;TZID=America/New_York:20221202T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Minje Kim (Indiana University) “Personalized Speech Enhancement: Da ta- and Resource-Efficient Machine Learning” URL:https://www.clsp.jhu.edu/events/minje-kim-indiana-university/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,December\,Kim END:VEVENT END:VCALENDAR