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:20240329T093706Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nNeural sequence generation systems oftentimes generate sequences by searching for the most likely se quence under the learnt probability distribution. This assumes that the mo st likely sequence\, i.e. the mode\, under such a model must also be the b est sequence it has to offer (often in a given context\, e.g. conditioned on a source sentence in translation). Recent findings in neural machine tr anslation (NMT) show that the true most likely sequence oftentimes is empt y under many state-of-the-art NMT models. This follows a large list of oth er pathologies and biases observed in NMT and other sequence generation mo dels: a length bias\, larger beams degrading performance\, exposure bias\, and many more. Many of these 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 t hese 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 over many translations\, and that the most likely translation oftentimes is a rare event. We further show that translation d istributions do capture important aspects of translation well in expectati on. Therefore\, we advocate for decision rules that take into account the entire probability distribution and not just its mode. We provide one exam ple of such a decision rule\, and show that this is a fruitful research di rection.
\nBiography
\nI am an assistant professor (UD) in natural language processing at the Institute for Logic\, Language and Computation where I lead the Probabilistic Language L earning group.
\nMy work concerns the design of models and algor ithms that learn to represent\, understand\, and generate language data. E xamples of specific problems I am interested in include language modelling \, machine translation\, syntactic parsing\, textual entailment\, text cla ssification\, and question answering.
\nI also develop techniques to approach general machine learning problems such as probabilistic inferenc e\, gradient and density estimation.
\nMy interests sit at the inter section of disciplines such as statistics\, machine learning\, approximate inference\, global optimization\, formal languages\, and computational li nguistics.
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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-TAGS;LANGUAGE=en-US:2021\,April\,Aziz END:VEVENT BEGIN:VEVENT UID:ai1ec-20987@www.clsp.jhu.edu DTSTAMP:20240329T093706Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
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 END:VCALENDAR