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-20117@www.clsp.jhu.edu DTSTAMP:20240329T100849Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nNeural sequence generation systems oftentimes generat e sequences by searching for the most likely sequence under the learnt pro bability distribution. This assumes that the most likely sequence\, i.e. t he mode\, under such a model must also be the best sequence it has to offe r (often in a given context\, e.g. conditioned on a source sentence in tra nslation). Recent findings in neural machine translation (NMT) show that t he true most likely sequence oftentimes is empty under many state-of-the-a rt NMT models. This follows a large list of other pathologies and biases o bserved in NMT and other sequence generation models: a length bias\, large r beams degrading performance\, exposure bias\, and many more. Many of the se works blame the probabilistic formulation of NMT or maximum likelihood estimation. We provide a different view on this: it is mode-seeking search \, e.g. beam search\, that introduces many of these pathologies and biases \, and such a decision rule is not suitable for the type of distributions learnt by NMT systems. We show that NMT models spread probability mass ove r many translations\, and that the most likely translation oftentimes is a rare event. We further show that translation distributions do capture imp ortant aspects of translation well in expectation. Therefore\, we advocate for decision rules that take into account the entire probability distribu tion and not just its mode. We provide one example of such a decision rule \, and show that this is a fruitful research direction.\nBiography\nI am a n assistant professor (UD) in natural language processing at the Institute for Logic\, Language and Computation where I lead the Probabilistic Langu age Learning group.\nMy work concerns the design of models and algorithms that learn to represent\, understand\, and generate language data. Example s of specific problems I am interested in include language modelling\, mac hine translation\, syntactic parsing\, textual entailment\, text classific ation\, and question answering.\nI also develop techniques to approach gen eral machine learning problems such as probabilistic inference\, gradient and density estimation.\nMy interests sit at the intersection of disciplin es such as statistics\, machine learning\, approximate inference\, global optimization\, formal languages\, and computational linguistics.\n \n DTSTART;TZID=America/New_York:20210419T120000 DTEND;TZID=America/New_York:20210419T131500 LOCATION:via Zoom SEQUENCE:0 SUMMARY:Wilker Aziz (University of Amsterdam) “The Inadequacy of the Mode in Neural Machine Translation” URL:https://www.clsp.jhu.edu/events/wilker-aziz-university-of-amsterdam/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nNeural sequence generation systems oftentimes generat e sequences by searching for the most likely sequence under the learnt pro bability distribution. This assumes that the most likely sequence\, i.e. t he mode\, under such a model must also be the best sequence it has to offe r (often in a given context\, e.g. conditioned on a source sentence in tra nslation). Recent findings in neural machine translation (NMT) show that t he true most likely sequence oftentimes is empty under many state-of-the-a rt NMT models. This follows a large list of other pathologies and biases o bserved in NMT and other sequence generation models: a length bias\, large r beams degrading performance\, exposure bias\, and many more. Many of the se works blame the probabilistic formulation of NMT or maximum likelihood estimation. We provide a different view on this: it is mode-seeking search \, e.g. beam search\, that introduces many of these pathologies and biases \, and such a decision rule is not suitable for the type of distributions learnt by NMT systems. We show that NMT models spread probability mass ove r many translations\, and that the most likely translation oftentimes is a rare event. We further show that translation distributions do capture imp ortant aspects of translation well in expectation. Therefore\, we advocate for decision rules that take into account the entire probability distribu tion and not just its mode. We provide one example of such a decision rule \, and show that this is a fruitful research direction.
\nBi ography
\nI am an assistant professor (UD) in natu ral language processing at the Institute for Logic\, Language and Computation where I lead the Probabilistic Language Learning group.
\nMy work concerns the design of models and algorithms that learn to represe nt\, understand\, and generate language data. Examples of specific problem s I am interested in include language modelling\, machine translation\, sy ntactic parsing\, textual entailment\, text classification\, and question answering.
\nI also develop techniques to approach general machine l earning problems such as probabilistic inference\, gradient and density es timation.
\nMy interests sit at the intersection of disciplines such as statistics\, machine learning\, approximate inference\, global optimiz ation\, formal languages\, and computational linguistics.
\n\n< p> \n X-TAGS;LANGUAGE=en-US:2021\,April\,Aziz END:VEVENT BEGIN:VEVENT UID:ai1ec-20723@www.clsp.jhu.edu DTSTAMP:20240329T100849Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nText simplification aims to help audiences read and u nderstand a piece of text through lexical\, syntactic\, and discourse modi fications\, while remaining faithful to its central idea and meaning. Than ks to large-scale parallel corpora derived from Wikipedia and News\, much of modern-day text simplification research focuses on sentence simplificat ion\, transforming original\, more complex sentences into simplified versi ons. In this talk\, I present new frontiers that focus on discourse operat ions. First\, we consider the challenging task of simplifying highly techn ical language\, in our case\, medical texts. We introduce a new corpus of parallel texts in English comprising technical and lay summaries of all pu blished evidence pertaining to different clinical topics. We then propose a new metric to quantify stylistic differentiates between the two\, and mo dels for paragraph-level simplification. Second\, we present the first dat a-driven study of inserting elaborations and explanations during simplific ation\, and illustrate the richness and complexities of this phenomenon.\n Biography\n\nJessy Li is an assistant professor in the Department of Lingu istics at UT Austin where she works on in computational linguistics and na tural language processing. Her work focuses on discourse processing\, text generation\, and language pragmatics in social media. She received her Ph .D. in 2017 from the University of Pennsylvania. She received an ACM SIGSO FT Distinguished Paper Award at FSE 2019\, an Area Chair Favorite at COLIN G 2018\, and a Best Paper nomination at SIGDIAL 2016.\nWeb: https://jessyl i.com DTSTART;TZID=America/New_York:20210917T120000 DTEND;TZID=America/New_York:20210917T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Jessy Li (University of Texas at Austin – Virtual Visit) “New Chall enges in Text Simplification” URL:https://www.clsp.jhu.edu/events/jessy-li-university-of-texas-at-austin/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n
Abstr act
\nText simplification aims to help audiences read and u nderstand a piece of text through lexical\, syntactic\, and discourse modi fications\, while remaining faithful to its central idea and meaning. Than ks to large-scale parallel corpora derived from Wikipedia and News\, much of modern-day text simplification research focuses on sentence simplificat ion\, transforming original\, more complex sentences into simplified versi ons. In this talk\, I present new frontiers that focus on discourse operat ions. First\, we consider the challenging task of simplifying highly techn ical language\, in our case\, medical texts. We introduce a new corpus of parallel texts in English comprising technical and lay summaries of all pu blished evidence pertaining to different clinical topics. We then propose a new metric to quantify stylistic differentiates between the two\, and mo dels for paragraph-level simplification. Second\, we present the first dat a-driven study of inserting elaborations and explanations during simplific ation\, and illustrate the richness and complexities of this phenomenon. p>\n
Biography
\nAbstr act
\nLarge language models (LLMs) have demonstrated incred ible power\, but they also possess vulnerabilities that can lead to misuse and potential attacks. In this presentation\, we will address two fundame ntal questions regarding the responsible utilization of LLMs: (1) How can we accurately identify AI-generated text? (2) What measures can safeguard the intellectual property of LLMs? We will introduce two recent watermarki ng techniques designed for text and models\, respectively. Our discussion will encompass the theoretical underpinnings that ensure the correctness o f watermark detection\, along with robustness against evasion attacks. Fur thermore\, we will showcase empirical evidence validating their effectiven ess. These findings establish a solid technical groundwork for policymaker s\, legal professionals\, and generative AI practitioners alike.
\n< strong>Biography
\nLei Li is an Assistant Professor in Lang uage Technology Institute at Carnegie Mellon University. He received Ph.D. from Carnegie Mellon University School of Computer Science. He is a recip ient of ACL 2021 Best Paper Award\, CCF Young Elite Award in 2019\, CCF di stinguished speaker in 2017\, Wu Wen-tsün AI prize in 2017\, and 2012 ACM SIGKDD dissertation award (runner-up)\, and is recognized as Notable Area Chair of ICLR 2023. Previously\, he was a faculty member at UC Santa Barba ra. Prior to that\, he founded ByteDance AI Lab in 2016 and led its resea rch in NLP\, ML\, Robotics\, and Drug Discovery. He launched ByteDance’s m achine translation system VolcTrans and AI writing system Xiaomingbot\, se rving one billion users.
\n X-TAGS;LANGUAGE=en-US:2023\,Li\,September END:VEVENT END:VCALENDAR