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-20987@www.clsp.jhu.edu DTSTAMP:20240329T073929Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nWhile there is a vast amount of text written about ne arly any topic\, this is often difficult for someone unfamiliar with a spe cific field to understand. Automated text simplification aims to reduce th e complexity of a document\, making it more comprehensible to a broader au dience. Much of the research in this field has traditionally focused on si mplification sub-tasks\, such as lexical\, syntactic\, or sentence-level s implification. However\, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many po or transformations\; on the other hand\, recent neural models\, while prod ucing grammatical output\, often do not make all needed changes to the ori ginal text. In this thesis\, I discuss novel approaches for improving lexi cal and sentence-level simplification systems. Regarding sentence simplifi cation models\, after noting that encouraging diversity at inference time leads to significant improvements\, I take a closer look at the idea of di versity and perform an exhaustive comparison of diverse decoding technique s on other generation tasks. I also discuss the limitations in the framing of current simplification tasks\, which prevent these models from yet bei ng practically useful. Thus\, I also propose a retrieval-based reformulati on of the problem. Specifically\, starting with a document\, I identify co ncepts critical to understanding its content\, and then retrieve documents relevant for each concept\, re-ranking them based on the desired complexi ty level.\nBiography\nI’m a research scientist at the HLTCOE at Johns Hopk ins University. My primary research interests are in language generation\, diverse and constrained decoding\, and information retrieval. During my P hD I focused mainly on the task of text simplification\, and now am workin g on formulating structured prediction problems as end-to-end generation t asks. I received my PhD in July 2021 from the University of Pennsylvania w ith Chris Callison-Burch and Marianna Apidianaki. DTSTART;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-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nWhile there is a vast amount of text written about ne arly any topic\, this is often difficult for someone unfamiliar with a spe cific field to understand. Automated text simplification aims to reduce th e complexity of a document\, making it more comprehensible to a broader au dience. Much of the research in this field has traditionally focused on si mplification sub-tasks\, such as lexical\, syntactic\, or sentence-level s implification. However\, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many po or transformations\; on the other hand\, recent neural models\, while prod ucing grammatical output\, often do not make all needed changes to the ori ginal text. In this thesis\, I discuss novel approaches for improving lexi cal and sentence-level simplification systems. Regarding sentence simplifi cation models\, after noting that encouraging diversity at inference time leads to significant improvements\, I take a closer look at the idea of di versity and perform an exhaustive comparison of diverse decoding technique s on other generation tasks. I also discuss the limitations in the framing of current simplification tasks\, which prevent these models from yet bei ng practically useful. Thus\, I also propose a retrieval-based reformulati on of the problem. Specifically\, starting with a document\, I identify co ncepts critical to understanding its content\, and then retrieve documents relevant for each concept\, re-ranking them based on the desired complexi ty level.
\nBiography
\nI ’m a research scientist at the HLTCOE at Johns Hopkins University. My prim ary research interests are in language generation\, diverse and constraine d decoding\, and information retrieval. During my PhD I focused mainly on the task of text simplification\, and now am working on formulating struct ured prediction problems as end-to-end generation tasks. I received my PhD in July 2021 from the University of Pennsylvania with Chris Callison-Burc h and Marianna Apidianaki.
\n\n X-TAGS;LANGUAGE=en-US:2021\,Kriz\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-22422@www.clsp.jhu.edu DTSTAMP:20240329T073929Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nZipf’s law is commonly glossed by the aphorism “infre quent words are frequent\,” but in practice\, it has often meant that ther e are three types of words: frequent\, infrequent\, and out-of-vocabulary (OOV). Speech recognition solved the problem of frequent words in 1970 (wi th dynamic time warping). Hidden Markov models worked well for moderately infrequent words\, but the problem of OOV words was not solved until sequ ence-to-sequence neural nets de-reified the concept of a word. Many other social phenomena follow power-law distributions. The number of native sp eakers of the N’th most spoken language\, for example\, is 1.44 billion ov er N to the 1.09. In languages with sufficient data\, we have shown that monolingual pre-training outperforms multilingual pre-training. In less-f requent languages\, multilingual knowledge transfer can significantly redu ce phone error rates. In languages with no training data\, unsupervised A SR methods can be proven to converge\, as long as the eigenvalues of the l anguage model are sufficiently well separated to be measurable. Other syst ems of social categorization may follow similar power-law distributions. Disability\, for example\, can cause speech patterns that were never seen in the training database\, but not all disabilities need do so. The inabi lity of speech technology to work for people with even common disabilities is probably caused by a lack of data\, and can probably be solved by find ing better modes of interaction between technology researchers and the com munities served by technology.\nBiography\nMark Hasegawa-Johnson is a Will iam L. Everitt Faculty Fellow of Electrical and Computer Engineering at th e University of Illinois in Urbana-Champaign. He has published research i n speech production and perception\, source separation\, voice conversion\ , and low-resource automatic speech recognition. DTSTART;TZID=America/New_York:20221209T120000 DTEND;TZID=America/New_York:20221209T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Mark Hasegawa-Johnson (University of Illinois Urbana-Champaign) “Zi pf’s Law Suggests a Three-Pronged Approach to Inclusive Speech Recognition ” URL:https://www.clsp.jhu.edu/events/mark-hasegawa-johnson-university-of-ill inois-urbana-champaign/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nZipf’s law is commonly glossed by the aphorism “infre quent words are frequent\,” but in practice\, it has often meant that ther e are three types of words: frequent\, infrequent\, and out-of-vocabulary (OOV). Speech recognition solved the problem of frequent words in 1970 (wi th dynamic time warping). Hidden Markov models worked well for moderately infrequent words\, but the problem of OOV words was not solved until sequ ence-to-sequence neural nets de-reified the concept of a word. Many other social phenomena follow power-law distributions. The number of native sp eakers of the N’th most spoken language\, for example\, is 1.44 billion ov er N to the 1.09. In languages with sufficient data\, we have shown that monolingual pre-training outperforms multilingual pre-training. In less-f requent languages\, multilingual knowledge transfer can significantly redu ce phone error rates. In languages with no training data\, unsupervised A SR methods can be proven to converge\, as long as the eigenvalues of the l anguage model are sufficiently well separated to be measurable. Other syst ems of social categorization may follow similar power-law distributions. Disability\, for example\, can cause speech patterns that were never seen in the training database\, but not all disabilities need do so. The inabi lity of speech technology to work for people with even common disabilities is probably caused by a lack of data\, and can probably be solved by find ing better modes of interaction between technology researchers and the com munities served by technology.
\nBiography
\nMark Hasegawa-Johnson is a William L. Everitt Faculty Fellow of Electrical and Computer Engineering at the University of Illinois in Urbana-Champaig n. He has published research in speech production and perception\, source separation\, voice conversion\, and low-resource automatic speech recogni tion.
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