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:20240329T133435Z 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-23314@www.clsp.jhu.edu DTSTAMP:20240329T133435Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nWhile GPT models have shown impressive performance on summarization and open-ended text generation\, it’s important to assess t heir abilities on more constrained text generation tasks that require sign ificant and diverse rewritings. In this talk\, I will discuss the challeng es of evaluating systems that are highly competitive and perform close to humans on two such tasks: (i) paraphrase generation and (ii) text simplifi cation. To address these challenges\, we introduce an interactive Rank-and -Rate evaluation framework. Our results show that GPT-3.5 has made a major step up from fine-tuned T5 in paraphrase generation\, but still lacks the diversity and creativity of humans who spontaneously produce large quanti ties of paraphrases.\nAdditionally\, we demonstrate that GPT-3.5 performs similarly to a single human in text simplification\, which makes it diffic ult for existing automatic evaluation metrics to distinguish between the t wo. To overcome this shortcoming\, we propose LENS\, a learnable evaluatio n metric that outperforms SARI\, BERTScore\, and other existing methods in both automatic evaluation and minimal risk decoding for text generation. \nBiography\nWei Xu is an assistant professor in the School of Interactive Computing at the Georgia Institute of Technology\, where she is also affi liated with the new NSF AI CARING Institute and Machine Learning Center. S he received her Ph.D. in Computer Science from New York University and her B.S. and M.S. from Tsinghua University. Xu’s research interests are in na tural language processing\, machine learning\, and social media\, with a f ocus on text generation\, stylistics\, robustness and controllability of m achine learning models\, and reading and writing assistive technology. She is a recipient of the NSF CAREER Award\, CrowdFlower AI for Everyone Awar d\, Criteo Faculty Research Award\, and Best Paper Award at COLING’18. She has also received funds from DARPA and IARPA. She is an elected member of the NAACL executive board and regularly serves as a senior area chair for AI/NLP conferences. DTSTART;TZID=America/New_York:20230224T120000 DTEND;TZID=America/New_York:20230224T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Wei Xu (Georgia Tech) “GPT-3 vs Humans: Rethinking Evaluation of Na tural Language Generation” URL:https://www.clsp.jhu.edu/events/wei-xu-georgia-tech/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n
Abstr act
\nWhile GPT mo dels have shown impressive performance on summarization and open-ended tex t generation\, it’s important to assess their abilities on more constraine d text generation tasks that require significant and diverse rewritings. I n this talk\, I will discuss the challenges of evaluating systems that are highly competitive and perform close to humans on two such tasks: (i) par aphrase generation and (ii) text simplification. To address these challeng es\, we introduce an interactive Rank-and-Rate evaluation framework. Our r esults show that GPT-3.5 has made a major step up from fine-tuned T5 in pa raphrase generation\, but still lacks the diversity and creativity of huma ns who spontaneously produce large quantities of paraphrases.
\nAdditionally\, we demon strate that GPT-3.5 performs similarly to a single human in text simplific ation\, which makes it difficult for existing automatic evaluation metrics to distinguish between the two. To overcome this shortcoming\, we propose LENS\, a learnable evaluation metric that outperforms SARI\, BERTScore\, and other existing methods in both automatic evaluation and minimal risk d ecoding for text generation.
\nBiography
\nWei Xu is an assis tant professor in the School of Interactive Computing at the Georgia Insti tute of Technology\, where she is also affiliated with the new NSF AI CARI NG Institute and Machine Learning Center. She received her Ph.D. in Comput er Science from New York University and her B.S. and M.S. from Tsinghua Un iversity. Xu’s research interests are in natural language processing\, mac hine learning\, and social media\, with a focus on text generation\, styli stics\, robustness and controllability of machine learning models\, and re ading and writing assistive technology. She is a recipient of the NSF CARE ER Award\, CrowdFlower AI for Everyone Award\, Criteo Faculty Research Awa rd\, and Best Paper Award at COLING’18. She has also received funds from D ARPA and IARPA. She is an elected member of the NAACL executive board and regularly serves as a senior area chair for AI/NLP conferences.
\n X-TAGS;LANGUAGE=en-US:2023\,February\,Xu END:VEVENT END:VCALENDAR