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:20240329T155601Z 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 END:VCALENDAR