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:20240329T070800Z 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-21277@www.clsp.jhu.edu DTSTAMP:20240329T070800Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nAs humans\, our understand
ing of language is grounded in a rich mental model about “how the world wo
rks” – that we learn through perception and interaction. We use this under
standing to reason beyond what we literally observe or read\, imagining ho
w situations might unfold in the world. Machines today struggle at this ki
nd of reasoning\, which limits how they can communicate with humans.
In my talk\, I will discuss th
ree lines of work to bridge this gap between machines and humans. I will f
irst discuss how we might measure grounded understanding. I will introduce
a suite of approaches for constructing benchmarks\, using machines in the
loop to filter out spurious biases. Next\, I will introduce PIGLeT: a mod
el that learns physical commonsense understanding by interacting with the
world through simulation\, using this knowledge to ground language. From a
n English-language description of an event\, PIGLeT can anticipate how the
world state might change – outperforming text-only models that are orders
of magnitude larger. Finally\, I will introduce MERLOT\, which learns abo
ut situations in the world by watching millions of YouTube videos with tra
nscribed speech. Through training objectives inspired by the developmental
psychology idea of multimodal reentry\, MERLOT learns to fuse language\,
vision\, and sound together into powerful representations. Together\, these directions suggest a pa
th forward for building machines that learn language rooted in the world.<
/p>\n
Biography
\nRowan Zellers is a final year P hD candidate at the University of Washington in Computer Science & Enginee ring\, advised by Yejin Choi and Ali Farhadi. His research focuses on enab ling machines to understand language\, vision\, sound\, and the world beyo nd these modalities. He has been recognized through an NSF Graduate Fellow ship and a NeurIPS 2021 outstanding paper award. His work has appeared in several media outlets\, including Wired\, the Washington Post\, and the Ne w York Times. In the past\, he graduated from Harvey Mudd College with a B .S. in Computer Science & Mathematics\, and has interned at the Allen Inst itute for AI.
DTSTART;TZID=America/New_York:20220214T120000 DTEND;TZID=America/New_York:20220214T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rowan Zellers (University of Washington) ” Grounding Language by Se eing\, Hearing\, and Interacting” URL:https://www.clsp.jhu.edu/events/rowan-zellers-university-of-washington- grounding-language-by-seeing-hearing-and-interacting/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,February\,Zellers END:VEVENT END:VCALENDAR