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:20240329T085909Z 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-23316@www.clsp.jhu.edu DTSTAMP:20240329T085909Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nUnderstanding the implications underlying a text is c ritical to assessing its impact\, in particular the social dynamics that m ay result from a reading of the text. This requires endowing artificial in telligence (AI) systems with pragmatic reasoning\, for example to correctl y conclude that the statement “Epidemics and cases of disease in the 21st century are “staged”” relates to unfounded conspiracy theories. In this ta lk\, I discuss how shortcomings in the ability of current AI systems to re ason about pragmatics present challenges to equitable detection of false o r harmful language. I demonstrate how these shortcomings can be addressed by imposing human-interpretable structure on deep learning architectures u sing insights from linguistics.In the first part of the talk\, I describe how adversarial text generation algorithms can be used to improve robustne ss of content moderation systems. I then introduce a pragmatic formalism f or reasoning about harmful implications conveyed by social media text. I s how how this pragmatic approach can be combined with generative neural lan guage models to uncover implications of news headlines. I also address the bottleneck to progress in text generation posed by gaps in evaluation of factuality. I conclude by showing how context-aware content moderation can be used to ensure safe interactions with conversational agents.\n \nBiogr aphy\nSaadia Gabriel is a PhD candidate in the Paul G. Allen School of Com puter Science & Engineering at the University of Washington\, advised by P rof. Yejin Choi and Prof. Franziska Roesner. Her researchrevolves around n atural language processing and machine learning\, with a particular focus on building systems for understanding how social commonsense manifests in text (i.e. how do people typically behave in social scenarios)\, as well a s mitigating spread of false or harmful text (e.g. Covid-19 misinformation ). Her work has been covered by a wide range of media outlets like Forbes and TechCrunch. It has also received a 2019 ACL best short paper nominatio n\, a 2019 IROS RoboCup best paper nomination and won a best paper award a t the 2020 WeCNLP summit. Prior to her PhD\, Saadia received a BA summa cu m laude from Mount Holyoke College in Computer Science and Mathematics.\n DTSTART;TZID=America/New_York:20230227T120000 DTEND;TZID=America/New_York:20230227T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Saadia Gabriel (University of Washington) “Socially Responsible and Factual Reasoning for Equitable AI Systems” URL:https://www.clsp.jhu.edu/events/saadia-gabriel-university-of-washington -socially-responsible-and-factual-reasoning-for-equitable-ai-systems/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n
Abstr act
\nUnderstanding the implications underlying a text is c
ritical to assessing its impact\, in particular the social dynamics that m
ay result from a reading of the text. This requires endowing artificial in
telligence (AI) systems with pragmatic reasoning\, for example to correctl
y conclude that the statement “Epidemics and cases of disease in the 21st
century are “staged”” relates to unfounded conspiracy theories. In this ta
lk\, I discuss how shortcomings in the ability of current AI systems to re
ason about pragmatics present challenges to equitable detection of false o
r harmful language. I demonstrate how these shortcomings can be addressed
by imposing human-interpretable structure on deep learning architectures u
sing insights from linguistics.
In the first part of the talk\, I describe how adversarial text gen
eration algorithms can be used to improve robustness of content moderation
systems. I then introduce a pragmatic formalism for reasoning about harmf
ul implications conveyed by social media text. I show how this pragmatic a
pproach can be combined with generative neural language models to uncover
implications of news headlines. I also address the bottleneck to progress
in text generation posed by gaps in evaluation of factuality. I conclude b
y showing how context-aware content moderation can be used to ensure safe
interactions with conversational agents.
\n
Biography
\nSaadia Gabr iel is a PhD candidate in the Paul G. Allen School of Computer Scie nce & Engineering at the University of Washington\, advised by Prof. Yejin Choi and Prof. Franziska Roesner. Her research re volves around natural language processing and machine learning\, with a pa rticular focus on building systems for understanding how social commonsens e manifests in text (i.e. how do people typically behave in social scenari os)\, as well as mitigating spread of false or harmful text (e.g. Covid-19 misinformation). Her work has been covered by a wide range of media outle ts like Forbes and TechCrunch. It has also received a 2019 ACL best short paper nomination\, a 2019 IROS RoboCup best paper nomination and won a bes t paper award at the 2020 WeCNLP summit. Prior to her PhD\, Saadia received a BA summa cum laude from Mount Holyoke College in Computer Sc ience and Mathematics.
\n\n X-TAGS;LANGUAGE=en-US:2023\,February\,Gabriel END:VEVENT END:VCALENDAR